diff --git a/HER-lastfm-sim[1].pkl b/HER-lastfm-sim[1].pkl
new file mode 100644
index 00000000..c0fcc086
--- /dev/null
+++ b/HER-lastfm-sim[1].pkl
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+tp98331
+a(g9952
+F0.6919633173809091
+tp98332
+as.
\ No newline at end of file
diff --git a/algorithm/ranking/APR.py b/algorithm/ranking/APR.py
index 441096ce..ff2c74fa 100644
--- a/algorithm/ranking/APR.py
+++ b/algorithm/ranking/APR.py
@@ -8,7 +8,7 @@
 try:
     import tensorflow as tf
 except ImportError:
-    print 'This method can only be run tensorflow!'
+    print 'This method can only run on tensorflow!'
     exit(-1)
 from tensorflow import set_random_seed
 set_random_seed(2)
diff --git a/algorithm/ranking/CDAE.py b/algorithm/ranking/CDAE.py
index 31484332..d427f4b9 100644
--- a/algorithm/ranking/CDAE.py
+++ b/algorithm/ranking/CDAE.py
@@ -6,7 +6,7 @@
 try:
     import tensorflow as tf
 except ImportError:
-    print 'This method can only be run tensorflow!'
+    print 'This method can only run on tensorflow!'
     exit(-1)
 from tensorflow import set_random_seed
 set_random_seed(2)
diff --git a/algorithm/ranking/DMF.py b/algorithm/ranking/DMF.py
index 5e4f3864..64fa454a 100644
--- a/algorithm/ranking/DMF.py
+++ b/algorithm/ranking/DMF.py
@@ -6,7 +6,7 @@
 try:
     import tensorflow as tf
 except ImportError:
-    print 'This method can only be run tensorflow!'
+    print 'This method can only run on tensorflow!'
     exit(-1)
 from tensorflow import set_random_seed
 set_random_seed(2)
diff --git a/algorithm/ranking/ExpoMF.py b/algorithm/ranking/ExpoMF.py
index af31b8b1..b69b40b6 100644
--- a/algorithm/ranking/ExpoMF.py
+++ b/algorithm/ranking/ExpoMF.py
@@ -22,9 +22,9 @@ def initModel(self):
         self.lam_theta = 1e-5
         self.lam_beta = 1e-5
         self.lam_y = 1.0
-        self.init_mu = 0.01
+        self.init_mu = 0.05
         self.a = 1.0
-        self.b = 99.0
+        self.b = 20.0
         self.init_std = 0.01
         self.theta = self.init_std * \
             np.random.randn(self.m, self.k).astype(np.float32)
@@ -52,6 +52,7 @@ def buildModel(self):
             print 'ITERATION #%d' % i
             self._update_factors(self.X, XT)
             self._update_expo(self.X, n_users)
+            self.ranking_performance()
 
 
     def _update_factors(self, X, XT):
diff --git a/algorithm/ranking/HER.py b/algorithm/ranking/HER.py
new file mode 100644
index 00000000..d68fb4aa
--- /dev/null
+++ b/algorithm/ranking/HER.py
@@ -0,0 +1,471 @@
+from baseclass.SocialRecommender import SocialRecommender
+from tool import config
+from random import randint
+from random import shuffle, choice
+from collections import defaultdict
+import numpy as np
+from tool.qmath import sigmoid, cosine, cosine_sp
+from math import log
+import gensim.models.word2vec as w2v
+
+
+class HER(SocialRecommender):
+    def __init__(self, conf, trainingSet=None, testSet=None, relation=None, fold='[1]'):
+        super(HER, self).__init__(conf, trainingSet, testSet, relation, fold)
+
+    def readConfiguration(self):
+        super(HER, self).readConfiguration()
+        options = config.LineConfig(self.config['HER'])
+        self.walkCount = int(options['-T'])
+        self.walkLength = int(options['-L'])
+        self.walkDim = int(options['-l'])
+        self.winSize = int(options['-w'])
+        self.topK = int(options['-k'])
+        self.alpha = float(options['-a'])
+        self.epoch = int(options['-ep'])
+        self.neg = int(options['-neg'])
+        self.rate = float(options['-r'])
+
+    def printAlgorConfig(self):
+        super(HER, self).printAlgorConfig()
+        print 'Specified Arguments of', self.config['recommender'] + ':'
+        print 'Walks count per user', self.walkCount
+        print 'Length of each walk', self.walkLength
+        print 'Dimension of user embedding', self.walkDim
+        print '=' * 80
+
+    def buildModel(self):
+        # tobeCleaned = []
+        # for user in self.dao.testSet_u:
+        #     for item in self.dao.testSet_u[user]:
+        #         if self.dao.testSet_u[user][item]<4.5:
+        #             tobeCleaned.append((user,item))
+        #
+        # for pair in tobeCleaned:
+        #     del self.dao.testSet_u[pair[0]][pair[1]]
+        #     if len(self.dao.testSet_u[pair[0]])==0:
+        #         del self.dao.testSet_u[pair[0]]
+        #data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followers:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followers[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followers[u]
+
+        for pair in cleanPair:
+            if self.sao.followers.has_key(pair[0]):
+                del self.sao.followers[pair[0]][pair[1]]
+
+        # li = self.sao.followees.keys()
+        #
+        print 'Kind Note: This method will probably take much time.'
+        # build U-F-NET
+        print 'Building weighted user-friend network...'
+        # filter isolated nodes and low ratings
+        # Definition of Meta-Path
+        p1 = 'UIU'
+        p2 = 'UFU'
+        p3 = 'UTU'
+        p4 = 'UFIU'
+        p5 = 'UFUIU'
+        mPaths = [p1,p2,p3,p4,p5]
+
+        self.G = np.random.rand(self.dao.trainingSize()[1], self.walkDim) / 10
+        self.W = np.random.rand(self.dao.trainingSize()[0], self.walkDim) / 10
+
+        self.fItems = {}  # filtered item set
+        for item in self.dao.trainSet_i:
+            self.fItems[item] = self.dao.trainSet_i[item].keys()
+
+        self.fBuying = {}  # filtered buying set
+        for user in self.dao.trainSet_u:
+            self.fBuying[user] = []
+            for item in self.dao.trainSet_u[user]:
+                if self.fItems.has_key(item) and self.dao.trainSet_u[user][item] > 0:
+                    self.fBuying[user].append(item)
+            if self.fBuying[user] == []:
+                del self.fBuying[user]
+
+
+        self.UFNet = defaultdict(list)
+
+        for user1 in self.sao.followees:
+            s1 = set(self.sao.followees[user1])
+            for user2 in self.sao.followees[user1]:
+                if self.sao.followees.has_key(user2):
+                    if user1 <> user2:
+                        s2 = set(self.sao.followees[user2])
+                        weight = len(s1.intersection(s2))
+                        self.UFNet[user1] += [user2] * (weight + 1)
+
+        self.UTNet = defaultdict(list)
+
+        for user1 in self.sao.followers:
+            s1 = set(self.sao.followers[user1])
+            for user2 in self.sao.followers[user1]:
+                if self.sao.followers.has_key(user2):
+                    if user1 <> user2:
+                        s2 = set(self.sao.followers[user2])
+                        weight = len(s1.intersection(s2))
+                        self.UTNet[user1] += [user2] * (weight + 1)
+        #
+        #
+        #
+        #
+        print 'Generating random meta-path random walks...'
+        self.walks = []
+        #self.usercovered = {}
+
+
+        for user in self.dao.user:
+
+            for mp in mPaths:
+                if mp == p1:
+                    self.walkCount = 10
+                if mp == p2:
+                    self.walkCount = 8
+                if mp == p3:
+                    self.walkCount = 8
+                if mp == p4:
+                    self.walkCount = 5
+                if mp == p5:
+                    self.walkCount = 5
+                for t in range(self.walkCount):
+
+                    path = ['U'+user]
+                    lastNode = user
+                    nextNode = user
+                    lastType = 'U'
+                    for i in range(self.walkLength / len(mp[1:])):
+
+                        for tp in mp[1:]:
+                            try:
+                                if tp == 'I':
+
+                                    nextNode = choice(self.fBuying[lastNode])
+
+                                if tp == 'U':
+
+                                    if lastType == 'I':
+                                        nextNode = choice(self.fItems[lastNode])
+                                    elif lastType == 'F':
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+                                    elif lastType == 'T':
+                                        nextNode = choice(self.UTNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UTNet[lastNode])
+
+
+
+                                if tp == 'F':
+
+                                    nextNode = choice(self.UFNet[lastNode])
+                                    while not self.dao.user.has_key(nextNode):
+                                        nextNode = choice(self.UFNet[lastNode])
+
+                                if tp == 'T':
+
+                                    nextNode = choice(self.UFNet[lastNode])
+                                    while not self.dao.user.has_key(nextNode):
+                                        nextNode = choice(self.UFNet[lastNode])
+
+                                path.append(tp+nextNode)
+                                lastNode = nextNode
+                                lastType = tp
+
+                            except (KeyError, IndexError):
+                                path = []
+                                break
+
+                    if path:
+                        self.walks.append(path)
+                        # for node in path:
+                        #     if node[1] == 'U' or node[1] == 'F':
+                        #         self.usercovered[node[0]] = 1
+                                # print path
+                                # if mp == 'UFIU':
+                                # pass
+        shuffle(self.walks)
+        uList = []
+        coldCount = 0
+        while len(uList)<1000:
+            cp = choice(self.walks)
+            su = choice(cp)
+            if su[0]=='I':
+                su = choice(cp)
+            if len(self.dao.trainSet_u[su[1:]])<10:
+                coldCount+=1
+            uList.append(su)
+        print 'cold rate:',float(coldCount)/1000
+
+
+        print 'walks:', len(self.walks)
+        # Training get top-k friends
+        print 'Generating user embedding...'
+        # print 'user covered', len(self.usercovered)
+        # print 'user coverage', float(len(self.usercovered)) / len(self.dao.user)
+        # sampleWalks = []
+        # sampleUser = {}
+        # for i in range(1000):
+        #     p = choice(self.walks)
+        #     u = choice(p)
+        #     while u[1]=='I':
+        #         u = choice(p)
+        #     if len(self.dao.trainSet_u[u[0]])<=10:
+        #         sampleUser[u[0]] = 1
+        #
+        # print 'user coverage:', len(sampleUser)/float(1000)
+
+
+
+
+        # iteration = 1
+        # userList = self.dao.user.keys()
+        # itemList = self.dao.item.keys()
+        self.topKSim = {}
+        # while iteration <= self.epoch:
+        #     loss = 0
+        #
+        #     for walk in self.walks:
+        #         for i, node in enumerate(walk):
+        #             neighbors = walk[max(0, i - self.winSize / 2):min(len(walk) - 1, i + self.winSize / 2)]
+        #             center, ctp = walk[i]
+        #             if ctp == 'U' or ctp == 'F' or ctp == 'T':  # user
+        #                 centerVec = self.W[self.dao.user[center]]
+        #             else:  # Item
+        #                 centerVec = self.G[self.dao.item[center]]
+        #             for entity, tp in neighbors:
+        #                 # negSamples = []
+        #                 currentVec = ''
+        #                 if tp == 'U' or tp == 'F' or tp=='T' and center <> entity:
+        #                     currentVec = self.W[self.dao.user[entity]]
+        #                     self.W[self.dao.user[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F' or ctp=='T':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(userList)
+        #                         while sample == entity:
+        #                             sample = choice(userList)
+        #                         sampleVec = self.W[self.dao.user[sample]]
+        #                         self.W[self.dao.user[sample]] -=   self.rate * (
+        #                             1 - sigmoid(-sampleVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F' or ctp == 'T':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -  log(sigmoid(-sampleVec.dot(centerVec)))
+        #                         # negSamples.append(choice)
+        #                 elif tp == 'I' and center <> entity:
+        #                     currentVec = self.G[self.dao.item[entity]]
+        #                     self.G[self.dao.item[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F' or ctp == 'T':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(itemList)
+        #                         while sample == entity:
+        #                             sample = choice(itemList)
+        #                         # negSamples.append(choice)
+        #                         sampleVec = self.G[self.dao.item[sample]]
+        #                         self.G[self.dao.item[sample]] -= self.rate * (
+        #                             1 - sigmoid(-currentVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F' or ctp == 'T':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -self.alpha * log(sigmoid(-sampleVec.dot(centerVec)))
+        #     shuffle(self.walks)
+        #
+        #     print 'iteration:', iteration, 'loss:', loss
+        #     iteration += 1
+
+        model = w2v.Word2Vec(self.walks, size=self.walkDim, window=5, min_count=0, iter=10)
+
+        for user in self.fBuying:
+            uid = self.dao.user[user]
+            self.W[uid] = model.wv['U'+user]
+        print 'User embedding generated.'
+
+        print 'Constructing similarity matrix...'
+        i = 0
+
+
+        for user1 in self.fBuying:
+            uSim = []
+            i+=1
+            if i%200==0:
+                print i,'/',len(self.fBuying)
+            vec1 = self.W[self.dao.user[user1]]
+            for user2 in self.fBuying:
+                if user1 <> user2:
+                    vec2 = self.W[self.dao.user[user2]]
+                    sim = cosine(vec1, vec2)
+                    uSim.append((user2,sim))
+
+            self.topKSim[user1] = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+
+
+        from tool import qmath
+        # for user1 in self.dao.user:
+        #     uSim = []
+        #     i += 1
+        #     if i % 200 == 0:
+        #         print i, '/', len(self.dao.user)
+        #     l1 = self.dao.trainSet_u[user1].keys()
+        #     for user2 in self.dao.user:
+        #         if user1 <> user2:
+        #             l2 = self.dao.trainSet_u[user2].keys()
+        #             sim = len(set(l1).intersection(l2))
+        #             if sim==0:
+        #                 continue
+        #             uSim.append((user2, sim))
+        #
+        #     self.topKSim[user1] = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+
+
+        # print 'Similarity matrix finished.'
+        # # # #print self.topKSim
+        #import pickle
+        # # # #
+        # # # #recordTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        # similarity = open('HER-lastfm-sim'+self.foldInfo+'.pkl', 'wb')
+        # vectors = open('HER-lastfm-vec'+self.foldInfo+'.pkl', 'wb')
+        # #Pickle dictionary using protocol 0.
+        #
+        # pickle.dump(self.topKSim, similarity)
+        # pickle.dump((self.W,self.G),vectors)
+        # similarity.close()
+        # vectors.close()
+
+        # matrix decomposition
+        #pkl_file = open('HER-lastfm-sim' + self.foldInfo + '.pkl', 'rb')
+
+        #self.topKSim = pickle.load(pkl_file)
+
+        print 'Decomposing...'
+        self.F = np.random.rand(self.dao.trainingSize()[0], self.k) / 10
+        # prepare Pu set, IPu set, and Nu set
+        print 'Preparing item sets...'
+        self.PositiveSet = defaultdict(dict)
+        self.IPositiveSet = defaultdict(dict)
+
+
+        for user in self.topKSim:
+            for item in self.dao.trainSet_u[user]:
+                self.PositiveSet[user][item]=1
+                # else:
+                #     self.NegativeSet[user].append(item)
+
+            for friend in self.topKSim[user]:
+                for item in self.dao.trainSet_u[friend[0]]:
+                    if not self.PositiveSet[user].has_key(item):
+                        self.IPositiveSet[user][item]=1
+
+        #print self.IPositiveSet
+        iteration = 0
+        self.s=1
+        while iteration < self.maxIter:
+            self.loss = 0
+            itemList = self.dao.item.keys()
+
+            for user in self.PositiveSet:
+                kItems = self.IPositiveSet[user].keys()
+                u = self.dao.user[user]
+
+                for item in self.PositiveSet[user]:
+                    i = self.dao.item[item]
+                    for ind in range(1):
+                        if len(kItems) > 0:
+                            item_k = choice(kItems)
+
+                            k = self.dao.item[item_k]
+                            self.optimization(u,i,k)
+
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet[user].has_key(item)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u,k,j)
+                        else:
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u, i, j)
+
+
+            for user in self.topKSim:
+                for friend in self.topKSim[user]:
+                    u = self.dao.user[user]
+                    f = self.dao.user[friend[0]]
+                    self.P[u] -= self.alpha*self.lRate*(self.P[u]-self.P[f])
+
+            self.loss += self.regU * (self.P * self.P).sum() + self.regI * (self.Q * self.Q).sum()
+            iteration += 1
+            if self.isConverged(iteration):
+                break
+
+    def optimization(self, u, i, j):
+        s = sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))
+        self.P[u] += self.lRate * (1 - s) * (self.Q[i] - self.Q[j])
+        self.Q[i] += self.lRate * (1 - s) * self.P[u]
+        self.Q[j] -= self.lRate * (1 - s) * self.P[u]
+        self.loss += -log(s)
+        self.P[u] -= self.lRate * self.regU * self.P[u]
+        self.Q[i] -= self.lRate * self.regI * self.Q[i]
+        self.Q[j] -= self.lRate * self.regI * self.Q[j]
+
+    def predict(self,user,item):
+
+        if self.dao.containsUser(user) and self.dao.containsItem(item):
+            u = self.dao.getUserId(user)
+            i = self.dao.getItemId(item)
+            predictRating = sigmoid(self.Q[i].dot(self.P[u]))
+            return predictRating
+        else:
+            return sigmoid(self.dao.globalMean)
+
+    def predictForRanking(self, u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.Q.dot(self.P[u])
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
\ No newline at end of file
diff --git a/algorithm/ranking/HERP.py b/algorithm/ranking/HERP.py
new file mode 100644
index 00000000..30e44ea0
--- /dev/null
+++ b/algorithm/ranking/HERP.py
@@ -0,0 +1,568 @@
+from baseclass.SocialRecommender import SocialRecommender
+from tool import config
+from random import randint
+from random import shuffle, choice
+from collections import defaultdict
+import numpy as np
+from tool.qmath import sigmoid, cosine, cosine_sp
+from math import log
+import gensim.models.word2vec as w2v
+
+
+class HERP(SocialRecommender):
+    def __init__(self, conf, trainingSet=None, testSet=None, relation=None, fold='[1]'):
+        super(HERP, self).__init__(conf, trainingSet, testSet, relation, fold)
+
+    def readConfiguration(self):
+        super(HERP, self).readConfiguration()
+        options = config.LineConfig(self.config['HERP'])
+        self.walkCount = int(options['-T'])
+        self.walkLength = int(options['-L'])
+        self.walkDim = int(options['-l'])
+        self.winSize = int(options['-w'])
+        self.topK = int(options['-k'])
+        self.alpha = float(options['-a'])
+        self.epoch = int(options['-ep'])
+        self.neg = int(options['-neg'])
+        self.rate = float(options['-r'])
+
+    def printAlgorConfig(self):
+        super(HERP, self).printAlgorConfig()
+        print 'Specified Arguments of', self.config['recommender'] + ':'
+        print 'Walks count per user', self.walkCount
+        print 'Length of each walk', self.walkLength
+        print 'Dimension of user embedding', self.walkDim
+        print '=' * 80
+
+    def readNegativeFeedbacks(self):
+        self.negative = defaultdict(list)
+        self.nItems = defaultdict(list)
+        filename = self.config['ratings'][:-4]+'_n.txt'
+        with open(filename) as f:
+            for line in f:
+                items = line.strip().split()
+                self.negative[items[0]].append(items[1])
+                self.nItems[items[1]].append(items[0])
+                if not self.dao.user.has_key(items[0]):
+                    self.dao.user[items[0]]=len(self.dao.user)
+                # if not self.dao.item.has_key(items[1]):
+                #     self.dao.item[items[1]]=len(self.dao.item)
+                #     self.dao.id2item[self.dao.item[items[1]]] = items[1]
+
+
+    def initModel(self):
+        super(HERP, self).initModel()
+        self.positive = defaultdict(list)
+        self.pItems = defaultdict(list)
+        for user in self.dao.trainSet_u:
+            for item in self.dao.trainSet_u[user]:
+                self.positive[user].append(item)
+                self.pItems[item].append(user)
+        self.readNegativeFeedbacks()
+        self.P = np.ones((len(self.dao.user), self.k)) / 10  # latent user matrix
+        #self.Q = np.ones((len(self.dao.item), self.k)) / 10  # latent item matrix
+
+
+
+
+    def buildModel(self):
+        # self.P = np.ones((self.dao.trainingSize()[0], self.k))/10  # latent user matrix
+        # self.Q = np.ones((self.dao.trainingSize()[1], self.k))/10  # latent item matrix
+        #data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followers:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followers[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followers[u]
+
+        for pair in cleanPair:
+            if self.sao.followers.has_key(pair[0]):
+                del self.sao.followers[pair[0]][pair[1]]
+
+        # li = self.sao.followees.keys()
+        #
+        import pickle
+        try:
+            self.trueTopKFriends = defaultdict(list)
+            pkl_file = open(self.config['ratings']+self.foldInfo+'p.pkl', 'rb')
+            self.pTopKSim = pickle.load(pkl_file)
+            pkl_file = open(self.config['ratings']+self.foldInfo+'n.pkl', 'rb')
+            self.nTopKSim = pickle.load(pkl_file)
+            self.trueTopKFriends = defaultdict(list)
+            for user in self.pTopKSim:
+                trueFriends = list(set(self.pTopKSim[user][:self.topK]).intersection(set(self.nTopKSim[user][:self.topK])))
+                self.trueTopKFriends[user] = trueFriends
+        except IOError:
+
+
+            print 'Kind Note: This method will probably take much time.'
+            # build U-F-NET
+            print 'Building weighted user-friend network...'
+            # filter isolated nodes and low ratings
+            # Definition of Meta-Path
+            p1 = 'UIU'
+            p2 = 'UFU'
+            p3 = 'UTU'
+            p4 = 'UFIU'
+            p5 = 'UFUIU'
+            mPaths = [p1,p2,p3,p4,p5]
+
+            self.G = np.random.rand(self.dao.trainingSize()[0], self.walkDim) / 10
+            self.W = np.random.rand(self.dao.trainingSize()[0], self.walkDim) / 10
+
+
+
+
+
+            self.UFNet = defaultdict(list)
+
+            for user1 in self.sao.followees:
+                s1 = set(self.sao.followees[user1])
+                for user2 in self.sao.followees[user1]:
+                    if self.sao.followees.has_key(user2):
+                        if user1 <> user2:
+                            s2 = set(self.sao.followees[user2])
+                            weight = len(s1.intersection(s2))
+                            self.UFNet[user1] += [user2] * (weight + 1)
+
+            self.UTNet = defaultdict(list)
+
+            for user1 in self.sao.followers:
+                s1 = set(self.sao.followers[user1])
+                for user2 in self.sao.followers[user1]:
+                    if self.sao.followers.has_key(user2):
+                        if user1 <> user2:
+                            s2 = set(self.sao.followers[user2])
+                            weight = len(s1.intersection(s2))
+                            self.UTNet[user1] += [user2] * (weight + 1)
+            #
+            #
+            #
+            #
+            print 'Generating random meta-path random walks... (Positive)'
+            self.pWalks = []
+            #self.usercovered = {}
+
+            # positive
+            for user in self.dao.user:
+
+                for mp in mPaths:
+                    if mp == p1:
+                        self.walkCount = 10
+                    if mp == p2:
+                        self.walkCount = 8
+                    if mp == p3:
+                        self.walkCount = 8
+                    if mp == p4:
+                        self.walkCount = 5
+                    if mp == p5:
+                        self.walkCount = 5
+                    for t in range(self.walkCount):
+
+                        path = ['U'+user]
+                        lastNode = user
+                        nextNode = user
+                        lastType = 'U'
+                        for i in range(self.walkLength / len(mp[1:])):
+
+                            for tp in mp[1:]:
+                                try:
+                                    if tp == 'I':
+
+                                        nextNode = choice(self.positive[lastNode])
+
+                                    if tp == 'U':
+
+                                        if lastType == 'I':
+                                            nextNode = choice(self.pItems[lastNode])
+                                        elif lastType == 'F':
+                                            nextNode = choice(self.UFNet[lastNode])
+                                            while not self.dao.user.has_key(nextNode):
+                                                nextNode = choice(self.UFNet[lastNode])
+                                        elif lastType == 'T':
+                                            nextNode = choice(self.UTNet[lastNode])
+                                            while not self.dao.user.has_key(nextNode):
+                                                nextNode = choice(self.UTNet[lastNode])
+
+
+
+                                    if tp == 'F':
+
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+
+                                    if tp == 'T':
+
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+
+                                    path.append(tp+nextNode)
+                                    lastNode = nextNode
+                                    lastType = tp
+
+                                except (KeyError, IndexError):
+                                    path = []
+                                    break
+
+                        if path:
+                            self.pWalks.append(path)
+                            # for node in path:
+                            #     if node[1] == 'U' or node[1] == 'F':
+                            #         self.usercovered[node[0]] = 1
+                                    # print path
+                                    # if mp == 'UFIU':
+                                    # pass
+            self.nWalks = []
+            # self.usercovered = {}
+
+            #negative
+            for user in self.dao.user:
+
+                for mp in mPaths:
+                    if mp == p1:
+                        self.walkCount = 10
+                    if mp == p2:
+                        self.walkCount = 8
+                    if mp == p3:
+                        self.walkCount = 8
+                    if mp == p4:
+                        self.walkCount = 5
+                    if mp == p5:
+                        self.walkCount = 5
+                    for t in range(self.walkCount):
+
+                        path = ['U' + user]
+                        lastNode = user
+                        nextNode = user
+                        lastType = 'U'
+                        for i in range(self.walkLength / len(mp[1:])):
+
+                            for tp in mp[1:]:
+                                try:
+                                    if tp == 'I':
+                                        nextNode = choice(self.negative[lastNode])
+
+                                    if tp == 'U':
+
+                                        if lastType == 'I':
+                                            nextNode = choice(self.nItems[lastNode])
+                                        elif lastType == 'F':
+                                            nextNode = choice(self.UFNet[lastNode])
+                                            while not self.dao.user.has_key(nextNode):
+                                                nextNode = choice(self.UFNet[lastNode])
+                                        elif lastType == 'T':
+                                            nextNode = choice(self.UTNet[lastNode])
+                                            while not self.dao.user.has_key(nextNode):
+                                                nextNode = choice(self.UTNet[lastNode])
+
+                                    if tp == 'F':
+
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+
+                                    if tp == 'T':
+
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+
+                                    path.append(tp + nextNode)
+                                    lastNode = nextNode
+                                    lastType = tp
+
+                                except (KeyError, IndexError):
+                                    path = []
+                                    break
+
+                        if path:
+                            self.nWalks.append(path)
+                            # for node in path:
+                            #     if node[1] == 'U' or node[1] == 'F':
+                            #         self.usercovered[node[0]] = 1
+                            # print path
+                            # if mp == 'UFIU':
+                            # pass
+            shuffle(self.pWalks)
+            print 'pwalks:', len(self.pWalks)
+            print 'nwalks:', len(self.nWalks)
+            # Training get top-k friends
+            print 'Generating user embedding...'
+
+            self.pTopKSim = {}
+            self.nTopKSim = {}
+
+            model = w2v.Word2Vec(self.pWalks, size=self.walkDim, window=5, min_count=0, iter=10)
+            model2 = w2v.Word2Vec(self.nWalks, size=self.walkDim, window=5, min_count=0, iter=10)
+
+            for user in self.positive:
+                uid = self.dao.user[user]
+                try:
+                    self.W[uid] = model.wv['U'+user]
+                except KeyError:
+                    continue
+
+            for user in self.negative:
+                uid = self.dao.user[user]
+                try:
+                    self.G[uid] = model2.wv['U'+user]
+                except KeyError:
+                    continue
+            print 'User embedding generated.'
+
+            print 'Constructing similarity matrix...'
+            i = 0
+
+
+            for user1 in self.positive:
+                uSim = []
+                i+=1
+                if i%200==0:
+                    print i,'/',len(self.positive)
+                vec1 = self.W[self.dao.user[user1]]
+                for user2 in self.positive:
+                    if user1 <> user2:
+                        vec2 = self.W[self.dao.user[user2]]
+                        sim = cosine(vec1, vec2)
+                        uSim.append((user2,sim))
+                fList = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+                self.pTopKSim[user1] = [item[0] for item in fList]
+
+            i=0
+            for user1 in self.negative:
+                uSim = []
+                i+=1
+                if i%200==0:
+                    print i,'/',len(self.negative)
+                vec1 = self.G[self.dao.user[user1]]
+                for user2 in self.negative:
+                    if user1 <> user2:
+                        vec2 = self.G[self.dao.user[user2]]
+                        sim = cosine(vec1, vec2)
+                        uSim.append((user2,sim))
+                fList = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+                self.nTopKSim[user1] = [item[0] for item in fList]
+
+
+            self.trueTopKFriends=defaultdict(list)
+            for user in self.pTopKSim:
+                trueFriends = list(set(self.pTopKSim[user]).intersection(set(self.nTopKSim[user])))
+                self.trueTopKFriends[user] = trueFriends
+            # if len(trueFriends)>0:
+            #     print trueFriends
+
+
+
+
+        # print 'Similarity matrix finished.'
+        # # # #print self.topKSim
+        import pickle
+        # # # #
+        # # # #recordTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        psimilarity = open(self.config['ratings']+self.foldInfo+'p.pkl', 'wb')
+        nsimilarity = open(self.config['ratings'] + self.foldInfo + 'n.pkl', 'wb')
+        # vectors = open('HERP-lastfm-vec'+self.foldInfo+'.pkl', 'wb')
+        # #Pickle dictionary using protocol 0.
+        #
+        pickle.dump(self.pTopKSim, psimilarity)
+        pickle.dump(self.nTopKSim, nsimilarity)
+        #pickle.dump((self.W,self.G),vectors)
+        # similarity.close()
+        # vectors.close()
+
+        # matrix decomposition
+
+
+
+        print 'Decomposing...'
+        self.F = np.random.rand(self.dao.trainingSize()[0], self.k) / 10
+        # prepare Pu set, IPu set, and Nu set
+        print 'Preparing item sets...'
+        self.PositiveSet = defaultdict(dict)
+        self.IPositiveSet = defaultdict(dict)
+        self.OKSet = defaultdict(dict)
+        self.NegSets = defaultdict(dict)
+
+        for user in self.dao.user:
+            for item in self.dao.trainSet_u[user]:
+                self.PositiveSet[user][item] = 1
+
+        for user in self.dao.user:
+            for item in self.negative[user]:
+                if self.dao.user.has_key(user) and self.dao.item.has_key(item):
+                    self.NegSets[user][item] = 1
+                    # else:
+                    #     self.NegativeSet[user].append(item)
+            if self.trueTopKFriends.has_key(user):
+                for friend in self.trueTopKFriends[user][:self.topK]:
+                    if self.dao.user.has_key(friend):
+                        for item in self.positive[friend]:
+                            if not self.PositiveSet[user].has_key(item) and not self.NegSets[user].has_key(item):
+                                if not self.IPositiveSet[user].has_key(item):
+                                    self.IPositiveSet[user][item] = 1
+                                else:
+                                    self.IPositiveSet[user][item] += 1
+
+            if self.pTopKSim.has_key(user):
+                for friend in self.pTopKSim[user][:self.topK]:
+                    if self.dao.user.has_key(friend):
+                        for item in self.positive[friend]:
+                            if not self.PositiveSet[user].has_key(item) and not self.IPositiveSet[user].has_key(item) and not self.NegSets[user].has_key(item):
+                                if not self.OKSet[user].has_key(item):
+                                    self.OKSet[user][item] = 1
+                                else:
+                                    self.OKSet[user][item] += 1
+
+            if self.nTopKSim.has_key(user):
+                for friend in self.nTopKSim[user][:self.topK]:
+                    if self.dao.user.has_key(friend):
+                        for item in self.negative[friend]:
+                            if self.dao.item.has_key(item):
+                                if not self.PositiveSet[user].has_key(item) and not self.IPositiveSet[user].has_key(item) \
+                                    and not self.OKSet.has_key(item):
+                                    if not self.NegSets[user].has_key(item):
+                                        self.NegSets[user][item]=1
+                                    else:
+                                        self.NegSets[user][item] += 1
+
+        iteration = 0
+
+        while iteration < self.maxIter:
+            self.loss = 0
+            itemList = self.dao.item.keys()
+
+            for user in self.PositiveSet:
+                #itemList = self.NegSets[user].keys()
+                kItems = self.IPositiveSet[user].keys()
+                okItems = self.OKSet[user].keys()
+                nItems = self.NegSets[user].keys()
+                u = self.dao.user[user]
+                # kc+= len(kItems)
+                # okc += len(okItems)
+                # nc+=len(nItems)
+                for item in self.PositiveSet[user]:
+                    i = self.dao.item[item]
+
+                    for ind in range(1):
+                        if len(kItems) > 0 and len(okItems) > 0:
+
+                            item_k = choice(kItems)
+
+                            k = self.dao.item[item_k]
+                            self.optimization(u,i,k)
+
+                            item_ok = choice(okItems)
+                            ok = self.dao.item[item_ok]
+
+                            self.optimization(u,k,ok)
+
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet[user].has_key(item_j) or self.OKSet[user].has_key(item_j)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u,ok,j)
+
+                        elif len(kItems)==0 and len(okItems)>0:
+                            item_ok = choice(okItems)
+                            ok = self.dao.item[item_ok]
+
+                            self.optimization(u,i,ok)
+
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet[user].has_key(item_j) or self.OKSet[user].has_key(item_j)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u,ok,j)
+
+                        elif len(kItems)>0 and len(okItems)==0:
+                            item_k = choice(kItems)
+                            k = self.dao.item[item_k]
+
+                            self.optimization(u,i,k)
+
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet[user].has_key(item_j) or self.OKSet[user].has_key(item_j)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u,k,j)
+
+                        else:
+                            item_j = choice(itemList)
+                            while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet[user].has_key(item_j) or
+                                   self.OKSet[user].has_key(item_j)):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u, i, j)
+                        if len(nItems)>0:
+                            item_n = choice(nItems)
+                            n = self.dao.item[item_n]
+                            self.optimization(u,j,n)
+
+                # for i in range(max(50-10,0)):
+                #     for friend in self.trueTopKFriends[user]:
+                #         u = self.dao.user[user]
+                #         f = self.dao.user[friend]
+                #         self.P[u] -= self.alpha*self.lRate*(self.P[u]-self.P[f])
+
+            for co in range(2):
+                for friend in self.trueTopKFriends[user]:
+                    u = self.dao.user[user]
+                    f = self.dao.user[friend]
+                    self.P[u] -= self.alpha*self.lRate*(self.P[u]-self.P[f])
+                    #self.loss += self.alpha * (self.P[u] - self.P[f]).dot((self.P[u] - self.P[f]))
+
+            self.loss += self.regU * (self.P * self.P).sum() + self.regI * (self.Q * self.Q).sum()
+
+            iteration += 1
+            if self.isConverged(iteration):
+                break
+
+    def optimization(self, u, i, j):
+        s = sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))
+        self.P[u] += self.lRate * (1 - s) * (self.Q[i] - self.Q[j])
+        self.Q[i] += self.lRate * (1 - s) * self.P[u]
+        self.Q[j] -= self.lRate * (1 - s) * self.P[u]
+        self.loss += -log(s)
+        self.P[u] -= self.lRate * self.regU * self.P[u]
+        self.Q[i] -= self.lRate * self.regI * self.Q[i]
+        self.Q[j] -= self.lRate * self.regI * self.Q[j]
+
+    def predict(self,user,item):
+
+        if self.dao.containsUser(user) and self.dao.containsItem(item):
+            u = self.dao.getUserId(user)
+            i = self.dao.getItemId(item)
+            predictRating = sigmoid(self.Q[i].dot(self.P[u]))
+            return predictRating
+        else:
+            return sigmoid(self.dao.globalMean)
+
+    def predictForRanking(self, u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.Q.dot(self.P[u])
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
\ No newline at end of file
diff --git a/algorithm/ranking/MERec.py b/algorithm/ranking/MERec.py
new file mode 100644
index 00000000..7b70691d
--- /dev/null
+++ b/algorithm/ranking/MERec.py
@@ -0,0 +1,236 @@
+from baseclass.IterativeRecommender import IterativeRecommender
+from scipy.sparse import *
+from scipy import *
+import numpy as np
+from numpy import linalg as LA
+from joblib import Parallel, delayed
+from math import sqrt
+
+EPS = 1e-8
+# this algorithm refers to the following paper:
+# #########----  Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation   ----#############
+# MERec_boost
+
+class MERec(IterativeRecommender):
+    def __init__(self,conf,trainingSet=None,testSet=None,fold='[1]'):
+        super(MERec, self).__init__(conf,trainingSet,testSet,fold)
+
+    def initModel(self):
+        super(MERec, self).initModel()
+        self.lam_theta = 1e-5
+        self.lam_beta = 1e-5
+        self.lam_y = 1.0
+        self.init_mu = 0.05
+        self.a = 1.0
+        self.b = 20.0
+        self.s= 5
+        self.init_std = 0.1
+        self.theta = self.init_std * \
+            np.random.randn(self.m, self.k).astype(np.float32)
+        self.beta = self.init_std * \
+            np.random.randn(self.n, self.k).astype(np.float32)
+        self.mu = self.init_mu * np.ones((self.m,self.n), dtype=np.float32)
+        self.n_jobs=4
+        self.batch_size=300
+        self.loadMetaInfo()
+        row,col,val = [],[],[]
+        for user in self.dao.trainSet_u:
+            for item in self.dao.trainSet_u[user]:
+                u = self.dao.user[user]
+                i = self.dao.item[item]
+                row.append(u)
+                col.append(i)
+                val.append(1)
+
+        self.X = csr_matrix((np.array(val),(np.array(row),np.array(col))),(self.m,self.n))
+
+        row,col,val = [],[],[]
+        for user in self.user2art:
+            for a in self.user2art[user]:
+                u = self.dao.user[user]
+                i = self.id['artist'][a]
+                row.append(u)
+                col.append(i)
+                val.append(1)
+        self.U2A = csr_matrix((np.array(val), (np.array(row), np.array(col))), (self.m, len(self.id['artist'])))
+
+        row,col,val = [],[],[]
+        for artist in self.art2song:
+            for item in self.art2song[artist]:
+                a = self.id['artist'][artist]
+                i = self.dao.item[item]
+                row.append(a)
+                col.append(i)
+                val.append(1.0/len(self.art2song[artist]))
+        self.A2S = csr_matrix((np.array(val), (np.array(row), np.array(col))), (len(self.id['artist']),self.n))
+
+        row,col,val = [],[],[]
+        for user in self.user2album:
+            for a in self.user2album[user]:
+                u = self.dao.user[user]
+                i = self.id['album'][a]
+                row.append(u)
+                col.append(i)
+                val.append(1)
+        self.U2Al = csr_matrix((np.array(val), (np.array(row), np.array(col))), (self.m, len(self.id['album'])))
+
+        row,col,val = [],[],[]
+        for album in self.album2song:
+            for item in self.album2song[album]:
+                a = self.id['album'][album]
+                i = self.dao.item[item]
+                row.append(a)
+                col.append(i)
+                val.append(1.0/len(self.album2song[album]))
+        self.Al2S = csr_matrix((np.array(val), (np.array(row), np.array(col))), (len(self.id['album']),self.n))
+
+        self.commuteMatrix_a = np.array(self.U2A.dot(self.A2S).todense())
+        self.commuteMatrix_al = np.array(self.U2Al.dot(self.Al2S).todense())
+
+    def loadMetaInfo(self):
+        from collections import defaultdict
+        self.user2art = defaultdict(dict)
+        self.art2song = defaultdict(dict)
+        self.user2album = defaultdict(dict)
+        self.album2song = defaultdict(dict)
+        self.id=defaultdict(dict)
+        with open(self.config['ratings']) as f:
+            for line in f:
+                items = line.strip().split(',')
+                if self.dao.trainSet_u.has_key(items[0]) and self.dao.trainSet_u[items[0]].has_key(items[1]):
+                    self.art2song[items[2]][items[1]]=1
+                    self.user2art[items[0]][items[2]]=1
+                    self.album2song[items[3]][items[1]]=1
+                    self.user2album[items[0]][items[3]]=1
+                    if items[2] not in self.id['artist']:
+                        self.id['artist'][items[2]]=len(self.id['artist'])
+                    if items[3] not in self.id['album']:
+                        self.id['album'][items[3]] = len(self.id['album'])
+
+
+    def buildModel(self):
+        print 'training...'
+        iteration = 0
+
+        self._update(self.X)
+
+    def _update(self, X):
+        '''Model training and evaluation on validation set'''
+        n_users = X.shape[0]
+        XT = X.T.tocsr()  # pre-compute this
+        self.vad_ndcg = -np.inf
+        for i in xrange(self.maxIter):
+
+            print 'ITERATION #%d' % i
+            self._update_factors(X, XT)
+            print self.mu
+            self._update_expo(X, n_users)
+            self.ranking_performance()
+
+
+    def _update_factors(self, X, XT):
+        '''Update user and item collaborative factors with ALS'''
+        self.theta = recompute_factors(self.beta, self.theta, X,
+                                       self.lam_theta / self.lam_y,
+                                       self.lam_y,
+                                       self.mu,
+                                       self.n_jobs,
+                                       batch_size=self.batch_size)
+
+        self.beta = recompute_factors(self.theta, self.beta, XT,
+                                      self.lam_beta / self.lam_y,
+                                      self.lam_y,
+                                      self.mu,
+                                      self.n_jobs,
+                                      batch_size=self.batch_size)
+
+
+    def _update_expo(self, X, n_users):
+        '''Update exposure prior'''
+        print '\tUpdating exposure prior...'
+
+        start_idx = range(0, n_users, self.batch_size)
+        end_idx = start_idx[1:] + [n_users]
+
+        A_sum = np.zeros(self.n)
+        for lo, hi in zip(start_idx, end_idx):
+            A_sum += a_row_batch(X[lo:hi], self.theta[lo:hi], self.beta,
+                                 self.lam_y, self.mu[lo:hi]).sum(axis=0)
+
+        A_sum=np.tile(A_sum,[self.m,1])
+        S_sum = 1*self.commuteMatrix_a*A_sum
+        S_sum += 0.2*self.commuteMatrix_al*A_sum
+        print  A_sum +self.s*S_sum
+        self.mu = (self.a + A_sum +self.s*S_sum- 1) / (self.a + self.b + self.s*S_sum+n_users - 2)
+
+
+    def predictForRanking(self,u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.beta.dot(self.theta[u])
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
+
+# Utility functions #
+
+
+
+def get_row(Y, i):
+    '''Given a scipy.sparse.csr_matrix Y, get the values and indices of the
+    non-zero values in i_th row'''
+    lo, hi = Y.indptr[i], Y.indptr[i + 1]
+    return Y.data[lo:hi], Y.indices[lo:hi]
+
+def a_row_batch(Y_batch, theta_batch, beta, lam_y, mu):
+    '''Compute the posterior of exposure latent variables A by batch'''
+    pEX = sqrt(lam_y / 2 * np.pi) * \
+          np.exp(-lam_y * theta_batch.dot(beta.T) ** 2 / 2)
+
+    A = (pEX + EPS) / (pEX + EPS + (1 - mu) / mu)
+    A[Y_batch.nonzero()] = 1.
+    return A
+
+def _solve(k, A_k, X, Y, f, lam, lam_y, mu):
+    '''Update one single factor'''
+    s_u, i_u = get_row(Y, k)
+    a = np.dot(s_u * A_k[i_u], X[i_u])
+    B = X.T.dot(A_k[:, np.newaxis] * X) + lam * np.eye(f)
+    return LA.solve(B, a)
+
+def _solve_batch(lo, hi, X, X_old_batch, Y, m, f, lam, lam_y, mu):
+    '''Update factors by batch, will eventually call _solve() on each factor to
+    keep the parallel process busy'''
+    assert X_old_batch.shape[0] == hi - lo
+
+    if mu.shape[1] == X.shape[0]:  # update users
+        A_batch = a_row_batch(Y[lo:hi], X_old_batch, X, lam_y, mu[lo:hi])
+    else:  # update items
+        A_batch = a_row_batch(Y[lo:hi], X_old_batch, X, lam_y, mu.T[lo:hi])
+
+    X_batch = np.empty_like(X_old_batch, dtype=X_old_batch.dtype)
+    for ib, k in enumerate(xrange(lo, hi)):
+        X_batch[ib] = _solve(k, A_batch[ib], X, Y, f, lam, lam_y, mu)
+    return X_batch
+
+def recompute_factors(X, X_old, Y, lam, lam_y, mu, n_jobs, batch_size=1000):
+    '''Regress X to Y with exposure matrix (computed on-the-fly with X_old) and
+    ridge term lam by embarrassingly parallelization. All the comments below
+    are in the view of computing user factors'''
+    m, n = Y.shape  # m = number of users, n = number of items
+    assert X.shape[0] == n
+    assert X_old.shape[0] == m
+    f = X.shape[1]  # f = number of factors
+
+    start_idx = range(0, m, batch_size)
+    end_idx = start_idx[1:] + [m]
+    res = Parallel(n_jobs=n_jobs)(delayed(_solve_batch)(
+        lo, hi, X, X_old[lo:hi], Y, m, f, lam, lam_y, mu)
+                                   for lo, hi in zip(start_idx, end_idx))
+    # res = []
+    # for lo, hi in zip(start_idx, end_idx):
+    #     res.append(_solve_batch(lo, hi, X, X_old[lo:hi], Y, m, f, lam, lam_y, mu))
+
+    X_new = np.vstack(res)
+    return X_new
+
diff --git a/algorithm/ranking/MPE_BPR.py b/algorithm/ranking/MPE_BPR.py
new file mode 100644
index 00000000..31d45116
--- /dev/null
+++ b/algorithm/ranking/MPE_BPR.py
@@ -0,0 +1,485 @@
+from baseclass.SocialRecommender import SocialRecommender
+from tool import config
+from random import randint
+from random import shuffle, choice
+from collections import defaultdict
+import numpy as np
+from tool.qmath import sigmoid, cosine,cosine_sp
+from math import log
+from structure.symmetricMatrix import SymmetricMatrix
+import os
+
+from time import localtime,time,strftime
+import matplotlib.pyplot as plt
+
+
+
+class MPE_BPR(SocialRecommender):
+    def __init__(self, conf, trainingSet=None, testSet=None, relation=None, fold='[1]'):
+        super(MPE_BPR, self).__init__(conf, trainingSet, testSet, relation, fold)
+
+
+
+    def readConfiguration(self):
+        super(MPE_BPR, self).readConfiguration()
+        options = config.LineConfig(self.config['CUNE-MF'])
+        self.walkCount = int(options['-T'])
+        self.walkLength = int(options['-L'])
+        self.walkDim = int(options['-l'])
+        self.winSize = int(options['-w'])
+        self.topK = int(options['-k'])
+        self.alpha = float(options['-a'])
+        self.epoch = int(options['-ep'])
+        self.neg = int(options['-neg'])
+        self.rate = float(options['-r'])
+
+    def printAlgorConfig(self):
+        super(MPE_BPR, self).printAlgorConfig()
+        print 'Specified Arguments of', self.config['recommender'] + ':'
+        print 'Walks count per user', self.walkCount
+        print 'Length of each walk', self.walkLength
+        print 'Dimension of user embedding', self.walkDim
+        print '=' * 80
+
+    def buildModel(self):
+        #data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+        li = self.sao.followees.keys()
+        for u in li:
+            if len(self.sao.followees[u]) <= 1:
+                del self.sao.followees[u]
+        print 'Kind Note: This method will probably take much time.'
+        # build U-F-NET
+        print 'Building weighted user-friend network...'
+        # filter isolated nodes and low ratings
+        # Definition of Meta-Path
+        p1 = 'UIU'
+        p2 = 'UFU'
+        p3 = 'UFIU'
+        p4 = 'UFUIU'
+        mPaths = [p1, p2, p3,p4]
+
+        self.G = np.random.rand(self.dao.trainingSize()[1], self.walkDim) / 10
+        self.W = np.random.rand(self.dao.trainingSize()[0], self.walkDim) / 10
+
+        self.fItems = {}  # filtered item set
+        for item in self.dao.trainSet_i:
+            #if len(self.dao.trainSet_i[item]) > 1:
+            self.fItems[item] = self.dao.trainSet_i[item].keys()
+
+        self.fBuying = {}  # filtered buying set
+        for user in self.dao.trainSet_u:
+            self.fBuying[user] = []
+            for item in self.dao.trainSet_u[user]:
+                if self.fItems.has_key(item) and self.dao.trainSet_u[user][item] > 0.75:
+                    self.fBuying[user].append(item)
+            if self.fBuying[user] == []:
+                del self.fBuying[user]
+        # self.filteredRatings = defaultdict(list)
+        # for item in self.fItems:
+        #     for user in self.fItems[item]:
+        #         if self.fItems[item][user]>0.75:
+        #             self.filteredRatings[user].append(item)
+
+        self.UFNet = defaultdict(list)
+
+        for user1 in self.sao.followees:
+            s1 = set(self.sao.followees[user1])
+            for user2 in self.sao.followees[user1]:
+                if self.sao.followees.has_key(user2):
+                    if user1 <> user2:
+                        s2 = set(self.sao.followees[user2])
+                        weight = len(s1.intersection(s2))
+                        self.UFNet[user1] += [user2] * (weight + 1)
+        #
+        #
+        #
+        #
+        print 'Generating random meta-path random walks...'
+        self.walks = []
+        # self.visited = defaultdict(dict)
+        self.usercovered = {}
+        for user in self.dao.user:
+
+
+            for mp in mPaths:
+                if mp==p1:
+                    self.walkCount = 20
+                if mp==p2:
+                    self.walkCount = 20
+                if mp==p3:
+                    self.walkCount = 3
+                if mp==p4:
+                    self.walkCount = 3
+                for t in range(self.walkCount):
+
+                    path = [(user, 'U')]
+                    lastNode = user
+                    nextNode = user
+                    lastType = 'U'
+                    for i in range(self.walkLength / len(mp)):
+
+                            for tp in mp[1:]:
+                                try:
+                                    if tp == 'I':
+                                        # if not self.fBuying.has_key(lastNode):
+                                        #     path = []
+                                        #     break
+                                        nextNode = choice(self.fBuying[lastNode])
+
+                                    if tp == 'U':
+                                        # if lastType=='':
+                                        #     nextNode = user
+                                        if lastType == 'I':
+                                            nextNode = choice(self.fItems[lastNode])
+                                        elif lastType == 'F':
+                                            nextNode = choice(self.UFNet[lastNode])
+                                            while not self.dao.user.has_key(nextNode):
+                                                nextNode = choice(self.UFNet[lastNode])
+                                        #self.usercovered[nextNode] = 1
+
+                                    if tp == 'F':
+                                        # if not self.UFNet.has_key(lastNode):
+                                        #     path = []
+                                        #     break
+                                        nextNode = choice(self.UFNet[lastNode])
+                                        while not self.dao.user.has_key(nextNode):
+                                            nextNode = choice(self.UFNet[lastNode])
+                                        #self.usercovered[nextNode] = 1
+                                    path.append((nextNode, tp))
+                                    lastNode = nextNode
+                                    lastType = tp
+
+                                except (KeyError, IndexError):
+                                    path = []
+                                    break
+
+
+
+                    if path:
+                        self.walks.append(path)
+                        for node in path:
+                            if node[1]=='U' or node[1]=='F':
+                                self.usercovered[node[0]]=1
+                        # print path
+                        # if mp == 'UFIU':
+                        # pass
+        shuffle(self.walks)
+        print 'walks:',len(self.walks)
+        # Training get top-k friends
+        print 'Generating user embedding...'
+        print 'user covered',len(self.usercovered)
+        print 'user coverage', float(len(self.usercovered)) / len(self.dao.user)
+        iteration = 1
+        userList = self.dao.user.keys()
+        itemList = self.dao.item.keys()
+        self.topKSim = {}
+        # while iteration <= self.epoch:
+        #     loss = 0
+        #
+        #     for walk in self.walks:
+        #         for i, node in enumerate(walk):
+        #             neighbors = walk[max(0, i - self.winSize / 2):min(len(walk) - 1, i + self.winSize / 2)]
+        #             center, ctp = walk[i]
+        #             if ctp == 'U' or ctp == 'F':  # user
+        #                 centerVec = self.W[self.dao.user[center]]
+        #             else:  # Item
+        #                 centerVec = self.G[self.dao.item[center]]
+        #             for entity, tp in neighbors:
+        #                 # negSamples = []
+        #                 currentVec = ''
+        #                 if tp == 'U' or tp == 'F' and center <> entity:
+        #                     currentVec = self.W[self.dao.user[entity]]
+        #                     self.W[self.dao.user[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(userList)
+        #                         while sample == entity:
+        #                             sample = choice(userList)
+        #                         sampleVec = self.W[self.dao.user[sample]]
+        #                         self.W[self.dao.user[sample]] -=   self.rate * (
+        #                             1 - sigmoid(-sampleVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -  log(sigmoid(-sampleVec.dot(centerVec)))
+        #                         # negSamples.append(choice)
+        #                 elif tp == 'I' and center <> entity:
+        #                     currentVec = self.G[self.dao.item[entity]]
+        #                     self.G[self.dao.item[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(itemList)
+        #                         while sample == entity:
+        #                             sample = choice(itemList)
+        #                         # negSamples.append(choice)
+        #                         sampleVec = self.G[self.dao.item[sample]]
+        #                         self.G[self.dao.item[sample]] -= self.rate * (
+        #                             1 - sigmoid(-currentVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -self.alpha * log(sigmoid(-sampleVec.dot(centerVec)))
+        #     shuffle(self.walks)
+        #
+        #     print 'iteration:', iteration, 'loss:', loss
+        #     iteration += 1
+        #     i=0
+            # for user1 in self.fBuying:
+            #     uSim = []
+            #     i += 1
+            #     if i % 200 == 0:
+            #         print i, '/', len(self.fBuying)
+            #         break
+            #     vec1 = self.W[self.dao.user[user1]]
+            #     for user2 in self.fBuying:
+            #         if user1 <> user2:
+            #             vec2 = self.W[self.dao.user[user2]]
+            #             sim = cosine(vec1, vec2)
+            #             uSim.append((user2, sim))
+            #
+            #     self.topKSim[user1] = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+            #
+            #
+            # count = 0
+            # overlap = 0
+            # for user in self.topKSim:
+            #     li = self.dao.trainSet_u[user].keys()
+            #     for f, s in self.topKSim[user]:
+            #
+            #         # print 'embedding similarity',f,s
+            #         # print 'preference similarity',f,cosine_sp(self.dao.trainSet_u[user],self.dao.trainSet_u[f])
+            #         # os.system('pause')
+            #
+            #         li1 =  self.dao.trainSet_u[f].keys()
+            #         overlap += len(set(li).intersection(set(li1)))
+            #         count+=1
+            # print 'embedding overlap', float(overlap) / count
+            #
+            #
+            # count = 0
+            # overlap = 0
+            # for user in self.topKSim:
+            #     li = self.dao.trainSet_u[user].keys()
+            #     if self.sao.followees.has_key(user):
+            #         for f in self.sao.followees[user]:
+            #             # print 'embedding similarity',f,s
+            #             # print 'preference similarity',f,cosine_sp(self.dao.trainSet_u[user],self.dao.trainSet_u[f])
+            #             # os.system('pause')
+            #
+            #             li1 = self.dao.trainSet_u[f].keys()
+            #             overlap += len(set(li).intersection(set(li1)))
+            #             count += 1
+            # print 'friend overlap', float(overlap) / count
+            #
+            #
+            #
+            #
+            # count = 0
+            # overlap = 0
+            # for user in self.topKSim:
+            #     li = self.dao.trainSet_u[user].keys()
+            #     for f, s in self.topKSim[user]:
+            #
+            #         # print 'embedding similarity',f,s
+            #         # print 'preference similarity',f,cosine_sp(self.dao.trainSet_u[user],self.dao.trainSet_u[f])
+            #         # os.system('pause')
+            #         user2 = choice(userList)
+            #         li1 = self.dao.trainSet_u[user2].keys()
+            #         overlap += len(set(li).intersection(set(li1)))
+            #         count += 1
+            #
+            # print 'random overlap', float(overlap) / count
+
+        print 'User embedding generated.'
+
+
+
+
+        print 'Constructing similarity matrix...'
+        # i = 0
+        #
+        #
+        # for user1 in self.fBuying:
+        #     uSim = []
+        #     i+=1
+        #     if i%200==0:
+        #         print i,'/',len(self.fBuying)
+        #     vec1 = self.W[self.dao.user[user1]]
+        #     for user2 in self.fBuying:
+        #         if user1 <> user2:
+        #             vec2 = self.W[self.dao.user[user2]]
+        #             sim = cosine(vec1, vec2)
+        #             uSim.append((user2,sim))
+        #
+        #     self.topKSim[user1] = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+        #
+        #
+        # print 'Similarity matrix finished.'
+        # # #print self.topKSim
+        import pickle
+        #
+        # recordTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        # similarity = open('MPE-Ciao-sim'+self.foldInfo+'.pkl', 'wb')
+        # vectors = open('vec'+recordTime+'.pkl', 'wb')
+        # #Pickle dictionary using protocol 0.
+        #
+        # pickle.dump(self.topKSim, similarity)
+        # pickle.dump((self.W,self.G),vectors)
+        # similarity.close()
+        # vectors.close()
+
+        # matrix decomposition
+        pkl_file = open('MPE-lastfm-sim'+self.foldInfo+'.pkl', 'rb')
+
+        self.topKSim = pickle.load(pkl_file)
+
+
+        print 'Decomposing...'
+        self.F = np.random.rand(self.dao.trainingSize()[0], self.k) / 10
+        # prepare Pu set, IPu set, and Nu set
+        print 'Preparing item sets...'
+        self.PositiveSet = defaultdict(dict)
+        self.IPositiveSet = defaultdict(dict)
+        # self.NegativeSet = defaultdict(list)
+
+        for user in self.topKSim:
+            for item in self.dao.trainSet_u[user]:
+                if self.dao.trainSet_u[user][item] >= 1:
+                    self.PositiveSet[user][item] = 1
+                    # else:
+                    #     self.NegativeSet[user].append(item)
+
+
+            for friend in self.topKSim[user][:self.topK]:
+                if self.dao.user.has_key(friend[0]):
+                    for item in self.dao.trainSet_u[friend[0]]:
+                        if not self.PositiveSet[user].has_key(item):
+                            if not self.IPositiveSet[user].has_key(item):
+                                self.IPositiveSet[user][item] = 1
+                            else:
+                                self.IPositiveSet[user][item] += 1
+        iteration = 0
+        while iteration < self.maxIter:
+            self.loss = 0
+            itemList = self.dao.item.keys()
+            for user in self.PositiveSet:
+                u = self.dao.user[user]
+                kItems = self.IPositiveSet[user].keys()
+                for item in self.PositiveSet[user]:
+                    i = self.dao.item[item]
+
+                    # if len(self.IPositiveSet[user]) > 0:
+                    #     item_k = choice(kItems)
+                    #     k = self.dao.item[item_k]
+                    #     Suk = self.IPositiveSet[user][item_k]
+                    #     self.P[u] += (1 / (Suk + 1)) * self.lRate * (
+                    #     1 - sigmoid((self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k])) / (Suk + 1))) * (
+                    #                      self.Q[i] - self.Q[k])
+                    #     self.Q[i] += (1 / (Suk + 1)) * self.lRate * (
+                    #     1 - sigmoid((self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k])) / (Suk + 1))) * \
+                    #                  self.P[u]
+                    #     self.Q[k] -= (1 / (Suk + 1)) * self.lRate * (
+                    #     1 - sigmoid((self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k])) / (Suk + 1))) * self.P[u]
+                    #
+                    #     item_j = ''
+                    #     # if len(self.NegativeSet[user])>0:
+                    #     #     item_j = choice(self.NegativeSet[user])
+                    #     # else:
+                    #     item_j = choice(itemList)
+                    #     while (self.PositiveSet[user].has_key(item_j) or self.IPositiveSet.has_key(item_j)):
+                    #         item_j = choice(itemList)
+                    #     j = self.dao.item[item_j]
+                    #     self.P[u] += (1 / self.alpha) * self.lRate * (
+                    #     1 - sigmoid((1 / self.alpha) * (self.P[u].dot(self.Q[k]) - self.P[u].dot(self.Q[j])))) * (
+                    #                  self.Q[k] - self.Q[j])
+                    #     self.Q[k] += (1 / self.alpha) * self.lRate * (
+                    #     1 - sigmoid((1 / self.alpha) * (self.P[u].dot(self.Q[k]) - self.P[u].dot(self.Q[j])))) * self.P[u]
+                    #     self.Q[j] -= (1 / self.alpha) * self.lRate * (
+                    #     1 - sigmoid((1 / self.alpha) * (self.P[u].dot(self.Q[k]) - self.P[u].dot(self.Q[j])))) * self.P[u]
+                    #
+                    #     self.P[u] -= self.lRate * self.regU * self.P[u]
+                    #     self.Q[i] -= self.lRate * self.regI * self.Q[i]
+                    #     self.Q[j] -= self.lRate * self.regI * self.Q[j]
+                    #     self.Q[k] -= self.lRate * self.regI * self.Q[k]
+                    #
+                    #     self.loss += -log(sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k]))) - \
+                    #                  log(sigmoid((1 / self.alpha) * (self.P[u].dot(self.Q[k]) - self.P[u].dot(self.Q[j]))))
+                    # else:
+                    item_j = choice(itemList)
+                    while (self.PositiveSet[user].has_key(item_j)):
+                        item_j = choice(itemList)
+                    j = self.dao.item[item_j]
+                    self.P[u] += self.lRate * (1 - sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))) * (
+                        self.Q[i] - self.Q[j])
+                    self.Q[i] += self.lRate * (1 - sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))) * \
+                                 self.P[u]
+                    self.Q[j] -= self.lRate * (1 - sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))) * \
+                                 self.P[u]
+
+                    self.P[u] -= self.lRate * self.regU * self.P[u]
+                    self.Q[i] -= self.lRate * self.regI * self.Q[i]
+                    self.Q[j] -= self.lRate * self.regI * self.Q[j]
+                    self.loss += -log(sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j])))
+
+            for user in self.topKSim:
+                for friend in self.topKSim[user]:
+                    u = self.dao.user[user]
+                    f = self.dao.user[friend[0]]
+                    self.P[u] -= self.alpha*self.lRate*(self.P[u]-self.P[f])
+
+            self.loss += self.regU * (self.P * self.P).sum() + self.regI * (self.Q * self.Q).sum()
+            iteration += 1
+            if self.isConverged(iteration):
+                break
+
+    def predict(self, u, i):
+        if self.dao.containsUser(u) and self.dao.containsItem(i):
+            return sigmoid(self.P[self.dao.user[u]].dot(self.Q[self.dao.item[i]]))
+        elif self.dao.containsUser(u) and not self.dao.containsItem(i):
+            return self.dao.userMeans[u]
+        elif not self.dao.containsUser(u) and self.dao.containsItem(i):
+            return self.dao.itemMeans[i]
+        else:
+            return self.dao.globalMean
+
+    def predictForRanking(self, u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.Q.dot(self.P[u])
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
\ No newline at end of file
diff --git a/algorithm/ranking/NPR.py b/algorithm/ranking/NPR.py
new file mode 100644
index 00000000..7a1bc399
--- /dev/null
+++ b/algorithm/ranking/NPR.py
@@ -0,0 +1,272 @@
+#coding:utf8
+from baseclass.DeepRecommender import DeepRecommender
+from baseclass.SocialRecommender import SocialRecommender
+import numpy as np
+import random
+from tool import config
+from collections import defaultdict
+try:
+    import tensorflow as tf
+except ImportError:
+    print 'This method can only be run tensorflow!'
+    exit(-1)
+from tensorflow import set_random_seed
+set_random_seed(2)
+
+class NPR(SocialRecommender,DeepRecommender):
+
+    # NPR：Adversarial Personalized Ranking for Recommendation
+
+    def __init__(self,conf,trainingSet=None,testSet=None,relation=None,fold='[1]'):
+        DeepRecommender.__init__(self, conf=conf, trainingSet=trainingSet, testSet=testSet, fold=fold)
+        SocialRecommender.__init__(self,conf=conf,trainingSet=trainingSet,testSet=testSet,relation=relation,fold=fold)
+
+
+    # def readConfiguration(self):
+    #     super(NPR, self).readConfiguration()
+        #data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followers:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followers[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followers[u]
+
+        for pair in cleanPair:
+            if self.sao.followers.has_key(pair[0]):
+                del self.sao.followers[pair[0]][pair[1]]
+
+        #build friend-item set
+        self.FSet_dict = defaultdict(dict)
+        self.FSet_list = defaultdict(list)
+
+        for user in self.sao.followees:
+            for friend in self.sao.followees[user]:
+                for item in self.dao.userRated(friend)[0]:
+                    if not self.dao.trainSet_u[user].has_key(item):
+                        self.FSet_dict[user][item]=1
+                        self.FSet_list[user].append(item)
+
+        self.users_without_relations = []
+        for user in self.dao.user:
+            if not self.FSet_dict.has_key(user):
+                self.users_without_relations.append(user)
+
+    def readConfiguration(self):
+        super(NPR, self).readConfiguration()
+        args = config.LineConfig(self.config['NPR'])
+        self.eps = float(args['-eps'])
+        self.regAdv = float(args['-regA'])
+        self.advEpoch = int(args['-advEpoch'])
+        self.negativeCount = 2
+
+
+    def _create_variables(self):
+        #perturbation vectors
+        self.adv_U = tf.Variable(tf.zeros(shape=[self.m, self.k]),dtype=tf.float32, trainable=False)
+        self.adv_V = tf.Variable(tf.zeros(shape=[self.n, self.k]),dtype=tf.float32, trainable=False)
+
+        self.neg_idx = tf.placeholder(tf.int32, [None], name="n_idx")
+        self.V_neg_embed = tf.nn.embedding_lookup(self.V, self.neg_idx)
+
+        self.friend_item_idx = tf.placeholder(tf.int32, [None], name="f_idx")
+        self.V_friend_embed = tf.nn.embedding_lookup(self.V, self.friend_item_idx)
+
+        #parameters
+        self.eps = tf.constant(self.eps,dtype=tf.float32)
+        self.regAdv = tf.constant(self.regAdv,dtype=tf.float32)
+
+    def _create_inference(self):
+        result = tf.subtract(tf.reduce_sum(tf.multiply(self.U_embed, self.V_embed), 1),
+                                  tf.reduce_sum(tf.multiply(self.U_embed, self.V_neg_embed), 1))
+        return result
+
+    def _create_friend_inference(self):
+        result = tf.subtract(tf.reduce_sum(tf.multiply(self.U_embed, self.V_embed), 1),
+                             tf.reduce_sum(tf.multiply(self.U_embed, self.V_friend_embed), 1))
+        result = tf.reduce_sum(tf.nn.softplus(-result))
+        result2 = tf.subtract(tf.reduce_sum(tf.multiply(self.U_embed, self.V_friend_embed), 1),
+                             tf.reduce_sum(tf.multiply(self.U_embed, self.V_neg_embed), 1))
+        result = tf.add(result,tf.reduce_sum(tf.nn.softplus(-result2)))
+
+        return result
+
+    def _create_adv_friend_inference(self):
+        self.U_plus_delta = tf.add(self.U_embed, tf.nn.embedding_lookup(self.adv_U, self.u_idx))
+        self.V_friend_plus_delta = tf.add(self.V_friend_embed, tf.nn.embedding_lookup(self.adv_V, self.friend_item_idx))
+        self.V_neg_plus_delta = tf.add(self.V_neg_embed, tf.nn.embedding_lookup(self.adv_V, self.neg_idx))
+
+
+        result = tf.subtract(tf.reduce_sum(tf.multiply(self.U_plus_delta, self.V_friend_plus_delta), 1),
+                             tf.reduce_sum(tf.multiply(self.U_plus_delta, self.V_neg_plus_delta), 1))
+        return result
+
+    def _create_adv_inference(self):
+        self.U_plus_delta = tf.add(self.U_embed, tf.nn.embedding_lookup(self.adv_U, self.u_idx))
+        self.V_plus_delta = tf.add(self.V_embed, tf.nn.embedding_lookup(self.adv_V, self.v_idx))
+        self.V_neg_plus_delta = tf.add(self.V_neg_embed, tf.nn.embedding_lookup(self.adv_V, self.neg_idx))
+        result = tf.subtract(tf.reduce_sum(tf.multiply(self.U_plus_delta, self.V_plus_delta), 1),
+                             tf.reduce_sum(tf.multiply(self.U_plus_delta, self.V_neg_plus_delta), 1))
+        return result
+
+    def _create_adversarial(self):
+        #get gradients of Delta
+        self.grad_U, self.grad_V = tf.gradients(self.loss_adv, [self.adv_U,self.adv_V])
+
+        # convert the IndexedSlice Data to Dense Tensor
+        self.grad_U_dense = tf.stop_gradient(self.grad_U)
+        self.grad_V_dense = tf.stop_gradient(self.grad_V)
+
+        # normalization: new_grad = (grad / |grad|) * eps
+        self.update_U = self.adv_U.assign(tf.nn.l2_normalize(self.grad_U_dense, 1) * self.eps)
+        self.update_V = self.adv_V.assign(tf.nn.l2_normalize(self.grad_V_dense, 1) * self.eps)
+
+
+    def _create_loss(self):
+        self.reg_lambda = tf.constant(self.regU, dtype=tf.float32)
+        self.loss = tf.reduce_sum(tf.nn.softplus(-self._create_inference()))
+        self.reg_loss = tf.add(tf.multiply(self.reg_lambda, tf.nn.l2_loss(self.U_embed)),
+                               tf.multiply(self.reg_lambda, tf.nn.l2_loss(self.V_embed)))
+
+        self.reg_loss = tf.add(tf.multiply(self.reg_lambda, tf.nn.l2_loss(self.U_embed)), self.reg_loss)
+        self.total_loss = tf.add(self.loss, self.reg_loss)
+
+        self.loss_friend = self._create_friend_inference()
+        self.total_friend_loss = tf.add(self.loss_friend, self.reg_loss)
+
+        #loss of adversarial training
+        self.loss_adv = tf.multiply(self.regAdv,tf.reduce_sum(tf.nn.softplus(-self._create_adv_friend_inference())))
+        self.loss_adv = tf.add(self.loss_friend,self.loss_adv)
+
+    def _create_optimizer(self):
+        self.optimizer = tf.train.AdamOptimizer(self.lRate)
+        self.train = self.optimizer.minimize(self.total_loss)
+
+        self.optimizer_friend = tf.train.AdamOptimizer(self.lRate)
+        self.train_friend = self.optimizer.minimize(self.total_friend_loss)
+
+        self.optimizer_adv = tf.train.AdamOptimizer(self.lRate)
+        self.train_adv = self.optimizer.minimize(self.loss_adv)
+
+
+    def initModel(self):
+        super(NPR, self).initModel()
+        self._create_variables()
+        self._create_loss()
+        self._create_adversarial()
+        self._create_optimizer()
+
+
+    def next_batch(self):
+        batch_idx = np.random.randint(self.train_size, size=self.batch_size)
+
+        users = [self.dao.trainingData[idx][0] for idx in batch_idx]
+        items = [self.dao.trainingData[idx][1] for idx in batch_idx]
+        user_idx,item_idx=[],[]
+        friend_item_idx,neg_item_idx = [],[]
+        for i,user in enumerate(users):
+            if not self.FSet_dict.has_key(user):
+                continue
+            f_item = random.choice(self.FSet_list[user])
+            while self.dao.trainSet_u[user].has_key(f_item):
+                f_item = random.choice(self.FSet_list[user])
+            for j in range(1): #negative sampling
+                item_j = random.randint(0,self.n-1)
+                while self.dao.trainSet_u[user].has_key(self.dao.id2item[item_j]) and self.FSet_dict[user].has_key(item_j):
+                    item_j = random.randint(0, self.n - 1)
+                user_idx.append(self.dao.user[user])
+                item_idx.append(self.dao.item[items[i]])
+                friend_item_idx.append(self.dao.item[f_item])
+                neg_item_idx.append(item_j)
+
+        return user_idx,item_idx,friend_item_idx,neg_item_idx
+
+
+    def next_batch_v2(self):
+        batch_idx = np.random.randint(self.train_size, size=self.batch_size)
+
+        users = [self.dao.trainingData[idx][0] for idx in batch_idx]
+        items = [self.dao.trainingData[idx][1] for idx in batch_idx]
+        user_idx,item_idx=[],[]
+        neg_item_idx = []
+        for i,user in enumerate(users):
+            for j in range(self.negativeCount): #negative sampling
+                item_j = random.randint(0,self.n-1)
+
+                while self.dao.trainSet_u[user].has_key(self.dao.id2item[item_j]):
+                    item_j = random.randint(0, self.n - 1)
+
+                user_idx.append(self.dao.user[user])
+                item_idx.append(self.dao.item[items[i]])
+                neg_item_idx.append(item_j)
+
+        return user_idx,item_idx,neg_item_idx
+
+    def buildModel(self):
+        print 'training...'
+        iteration = 0
+        with tf.Session() as sess:
+            init = tf.global_variables_initializer()
+            sess.run(init)
+
+            # train the model until converged
+            for epoch in range(self.maxIter):
+                user_idx, item_idx, neg_item_idx = self.next_batch_v2()
+                _,loss = sess.run([self.train,self.total_loss],feed_dict={self.u_idx: user_idx, self.v_idx: item_idx, self.neg_idx:neg_item_idx})
+
+                user_idx, item_idx, friend_item_idx, neg_item_idx = self.next_batch()
+                _,loss = sess.run([self.train_friend, self.total_friend_loss],feed_dict={self.u_idx: user_idx, self.v_idx: item_idx, self.friend_item_idx:friend_item_idx,self.neg_idx: neg_item_idx})
+
+                print 'iteration:', epoch, 'loss:',loss
+
+                self.P = sess.run(self.U)
+                self.Q = sess.run(self.V)
+                self.ranking_performance()
+
+            # start adversarial training
+            for epoch in range(self.advEpoch):
+
+                user_idx, item_idx, neg_item_idx = self.next_batch_v2()
+                _, loss = sess.run([self.train, self.total_loss],
+                                   feed_dict={self.u_idx: user_idx, self.v_idx: item_idx, self.neg_idx: neg_item_idx})
+
+                user_idx,item_idx,friend_item_idx,neg_item_idx = self.next_batch()
+                sess.run([self.update_U, self.update_V],
+                         feed_dict={self.u_idx: user_idx, self.v_idx: item_idx, self.friend_item_idx:friend_item_idx,self.neg_idx: neg_item_idx})
+                _,loss = sess.run([self.train_adv,self.loss_adv],feed_dict={self.u_idx: user_idx, self.v_idx: item_idx,self.friend_item_idx:friend_item_idx, self.neg_idx:neg_item_idx})
+
+                print 'iteration:', epoch, 'loss:',loss
+
+                self.P = sess.run(self.U)
+                self.Q = sess.run(self.V)
+                self.ranking_performance()
+
+
+    def predictForRanking(self, u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.Q.dot(self.P[u])
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
+
+
diff --git a/algorithm/ranking/SERec.py b/algorithm/ranking/SERec.py
index 02874a0e..542982f5 100644
--- a/algorithm/ranking/SERec.py
+++ b/algorithm/ranking/SERec.py
@@ -55,8 +55,6 @@ def initModel(self):
 
     def buildModel(self):
         print 'training...'
-        iteration = 0
-
         self._update(self.X)
 
     def _update(self, X):
diff --git a/algorithm/ranking/WRMF.py b/algorithm/ranking/WRMF.py
index 1c289400..31999c3e 100644
--- a/algorithm/ranking/WRMF.py
+++ b/algorithm/ranking/WRMF.py
@@ -71,6 +71,7 @@ def buildModel(self):
             print 'iteration:',iteration,'loss:',self.loss
             # if self.isConverged(iteration):
             #     break
+            self.ranking_performance()
 
     def predictForRanking(self,u):
         'invoked to rank all the items for the user'
diff --git a/algorithm/rating/FM_BPR.py b/algorithm/rating/FM_BPR.py
new file mode 100644
index 00000000..0acbdf90
--- /dev/null
+++ b/algorithm/rating/FM_BPR.py
@@ -0,0 +1,240 @@
+from baseclass.SocialRecommender import SocialRecommender
+from math import log
+import numpy as np
+from tool import config
+from tool.qmath import sigmoid
+from random import choice
+from collections import defaultdict
+class FM_BPR(SocialRecommender):
+    def __init__(self,conf,trainingSet=None,testSet=None,relation=list(),fold='[1]'):
+        super(FM_BPR, self).__init__(conf,trainingSet,testSet,relation,fold)
+
+    def readConfiguration(self):
+        super(FM_BPR, self).readConfiguration()
+        options = config.LineConfig(self.config['FM_BPR'])
+        self.lambda_f = float(options['-f'])
+        self.lambda_m = float(options['-m'])
+
+    def initModel(self):
+        super(FM_BPR, self).initModel()
+        print 'Kind Note: This method will probably take much time.'
+
+        # data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followers:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followers[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followers[u]
+
+        for pair in cleanPair:
+            if self.sao.followers.has_key(pair[0]):
+                del self.sao.followers[pair[0]][pair[1]]
+
+        # build MNET,FNET
+        print 'Building Membership and Friendship networks...'
+
+        self.MNet = defaultdict(dict)
+
+        for user1 in self.dao.trainSet_u:
+            s1 = set(self.dao.trainSet_u[user1].keys())
+            for user2 in self.dao.trainSet_u:
+                s2 = set(self.dao.trainSet_u[user2].keys())
+                if user1==user2:
+                    continue
+                weight=len(s1.intersection(s2))
+                if weight>3:
+                    self.MNet[user1][user2]=1+log(weight)
+
+        self.FNet = defaultdict(dict)
+        for user1 in self.sao.user:
+            if self.dao.containsUser(user1):
+                s1 = set(self.sao.followees[user1].keys())
+                for user2 in s1:
+                    if self.dao.containsUser(user2):
+                        s2 = set(self.sao.followees[user2].keys())
+                        weight = len(s1.intersection(s2))
+                        if weight>1:
+                            self.FNet[user1][user2]=1+log(weight)
+                        else:
+                            self.FNet[user1][user2] = 1
+
+
+    def buildModel(self):
+        super(FM_BPR, self).buildModel()
+        self.M1 = np.random.random((self.k,self.k))/5
+        self.M2 = np.random.random((self.k,self.k))/5
+        self.Z = np.random.random((self.dao.trainingSize()[0],self.k))/10
+        self.F = np.random.random((self.dao.trainingSize()[0],self.k))/10
+
+        self.b = np.random.random(self.dao.trainingSize()[1])
+        print 'Preparing item sets...'
+        self.PositiveSet = defaultdict(dict)
+        self.JointSet = defaultdict(dict)
+        self.OkSet = defaultdict(dict)
+
+
+        for user in self.dao.user:
+            for item in self.dao.trainSet_u[user]:
+                if self.dao.trainSet_u[user][item] >= 1:
+                    self.PositiveSet[user][item] = 1
+                    # else:
+                    #     self.NegativeSet[user].append(item)
+
+        for user in self.dao.user:
+            if self.MNet.has_key(user) and self.FNet.has_key(user):
+                for friend in self.MNet[user]:
+                    if self.FNet[user].has_key(friend):
+                        for item in self.PositiveSet[friend]:
+                            self.JointSet[user][item]=1
+
+        for user in self.dao.user:
+            if self.MNet.has_key(user):
+                for friend in self.MNet[user]:
+                    for item in self.PositiveSet[friend]:
+                        if not self.JointSet[user].has_key(item):
+                            self.OkSet[user][item] = 1
+
+        for user in self.dao.user:
+            if self.FNet.has_key(user):
+                for friend in self.FNet[user]:
+                    for item in self.PositiveSet[friend]:
+                        if not self.JointSet[user].has_key(item):
+                            self.OkSet[user][item] = 1
+
+
+        print 'Training...'
+        iteration = 0
+        self.s=1
+        while iteration < self.maxIter:
+            self.loss = 0
+            itemList = self.dao.item.keys()
+
+            for user in self.PositiveSet:
+                jItems = self.JointSet[user].keys()
+                kItems = self.OkSet[user].keys()
+                u = self.dao.user[user]
+                for item in self.PositiveSet[user]:
+                    i = self.dao.item[item]
+
+                    for ind in range(1):
+                        if len(jItems) and len(kItems)>0:
+                            item_j = choice(jItems)
+                            j = self.dao.item[item_j]
+                            self.optimization(u,i,j)
+                            item_k = choice(kItems)
+                            k = self.dao.item[item_k]
+                            self.optimization(u, j, k)
+                            item_n = choice(itemList)
+                            while self.PositiveSet[user].has_key(item_n) or self.JointSet[user].has_key(item_n)\
+                                or self.OkSet[user].has_key(item_n):
+                                item_n = choice(itemList)
+                            n = self.dao.item[item_n]
+                            self.optimization(u,k,n)
+                            # self.loss += -log(sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k]))) - \
+                            #              log(sigmoid(
+                            #                  (1 / self.s) * (self.P[u].dot(self.Q[k]) - self.P[u].dot(self.Q[j]))))
+                        elif len(kItems)>0:
+                            item_k = choice(kItems)
+                            k = self.dao.item[item_k]
+                            self.optimization(u, i, k)
+                            item_n = choice(itemList)
+                            while self.PositiveSet[user].has_key(item_n) or self.JointSet[user].has_key(item_n)\
+                                or self.OkSet[user].has_key(item_n):
+                                item_n = choice(itemList)
+                            n = self.dao.item[item_n]
+                            self.optimization(u,k,n)
+                        elif len(jItems)>0:
+                            item_j = choice(jItems)
+                            j = self.dao.item[item_j]
+                            self.optimization(u, i, j)
+                            item_n = choice(itemList)
+                            while self.PositiveSet[user].has_key(item_n) or self.JointSet[user].has_key(item_n)\
+                                or self.OkSet[user].has_key(item_n):
+                                item_n = choice(itemList)
+                            n = self.dao.item[item_n]
+                            self.optimization(u,j,n)
+
+                        else:
+                            item_j = choice(itemList)
+                            while self.PositiveSet[user].has_key(item_j):
+                                item_j = choice(itemList)
+                            j = self.dao.item[item_j]
+                            self.optimization(u, i, j)
+
+                for friend in self.FNet[user]:
+                    f = self.dao.user[friend]
+                    err1 = self.FNet[user][friend]-self.P[u].dot(self.M1).dot(self.F[f])
+                    self.P[u]+=self.lRate*self.lambda_f*err1*self.M1.dot(self.F[f])
+                    self.F[f]+=self.lRate*self.lambda_f * err1 * self.M1.dot(self.P[u])
+                    self.M1+=self.lRate*self.lambda_f * err1 * self.P[u].reshape(self.k,1).dot(self.F[f].reshape(1,self.k))
+                    #self.loss+=err1**2
+                for member in self.MNet[user]:
+                    m = self.dao.user[member]
+                    err2 = self.MNet[user][member]-self.P[u].dot(self.M2).dot(self.Z[m])
+                    self.P[u]+=self.lRate*self.lambda_m*err2*self.M2.dot(self.Z[m])
+                    self.Z[m]+=self.lRate*self.lambda_m*err2*self.M2.dot(self.P[u])
+                    self.M2 += self.lRate * self.lambda_m*err2*self.P[u].reshape(self.k,1).dot(self.Z[m].reshape(1,self.k))
+                    self.loss += err2**2
+            # for user in self.dao.user:
+            #     if not self.sao.followees.has_key(user):
+            #         continueg
+            #     for friend in self.sao.followees[user]:
+            #         u = self.dao.user[user]
+            #         if not self.dao.user.has_key(friend):
+            #             continue
+            #         f = self.dao.user[friend]
+            #         self.P[u] -= 0.1*self.lRate*(self.P[u]-self.P[f])
+            self.loss += self.regU * (self.P * self.P).sum() + self.regI * (self.Q * self.Q).sum()#+self.b.dot(self.b)
+            iteration += 1
+            if self.isConverged(iteration):
+                break
+
+    def optimization(self, u, i, j):
+        s = sigmoid(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[j]))
+        self.P[u] += self.lRate * (1 - s) * (self.Q[i] - self.Q[j])
+        self.Q[i] += self.lRate * (1 - s) * self.P[u]
+        self.Q[j] -= self.lRate * (1 - s) * self.P[u]
+        self.loss += -log(s)
+        self.P[u] -= self.lRate * self.regU * self.P[u]
+        self.Q[i] -= self.lRate * self.regI * self.Q[i]
+        self.Q[j] -= self.lRate * self.regI * self.Q[j]
+
+    def predict(self,user,item):
+
+        if self.dao.containsUser(user) and self.dao.containsItem(item):
+            u = self.dao.getUserId(user)
+            i = self.dao.getItemId(item)
+            predictRating = sigmoid(self.Q[i].dot(self.P[u]))#+self.b[i])
+            return predictRating
+        else:
+            return sigmoid(self.dao.globalMean)
+
+    def predictForRanking(self, u):
+        'invoked to rank all the items for the user'
+        if self.dao.containsUser(u):
+            u = self.dao.getUserId(u)
+            return self.Q.dot(self.P[u])#+self.b
+        else:
+            return [self.dao.globalMean] * len(self.dao.item)
+
+
diff --git a/algorithm/rating/MPE_MF.py b/algorithm/rating/MPE_MF.py
new file mode 100644
index 00000000..89301c8a
--- /dev/null
+++ b/algorithm/rating/MPE_MF.py
@@ -0,0 +1,358 @@
+from baseclass.SocialRecommender import SocialRecommender
+from tool import config
+from random import randint
+from random import shuffle, choice
+from collections import defaultdict
+import numpy as np
+from tool.qmath import sigmoid, cosine
+from math import log
+from structure.symmetricMatrix import SymmetricMatrix
+from time import localtime,time,strftime
+import matplotlib.pyplot as plt
+
+
+
+class MPE_MF(SocialRecommender):
+    def __init__(self, conf, trainingSet=None, testSet=None, relation=None, fold='[1]'):
+        super(MPE_MF, self).__init__(conf, trainingSet, testSet, relation, fold)
+        self.nonLeafVec = {}
+        self.leafVec = {}
+
+
+    def readConfiguration(self):
+        super(MPE_MF, self).readConfiguration()
+        options = config.LineConfig(self.config['CUNE-MF'])
+        self.walkCount = int(options['-T'])
+        self.walkLength = int(options['-L'])
+        self.walkDim = int(options['-l'])
+        self.winSize = int(options['-w'])
+        self.topK = int(options['-k'])
+        self.alpha = float(options['-a'])
+        self.epoch = int(options['-ep'])
+        self.neg = int(options['-neg'])
+        self.rate = float(options['-r'])
+
+    def printAlgorConfig(self):
+        super(MPE_MF, self).printAlgorConfig()
+        print 'Specified Arguments of', self.config['recommender'] + ':'
+        print 'Walks count per user', self.walkCount
+        print 'Length of each walk', self.walkLength
+        print 'Dimension of user embedding', self.walkDim
+        print '=' * 80
+
+    def buildModel(self):
+        #data clean
+        cleanList = []
+        cleanPair = []
+        for user in self.sao.followees:
+            if not self.dao.user.has_key(user):
+                cleanList.append(user)
+            for u2 in self.sao.followees[user]:
+                if not self.dao.user.has_key(u2):
+                    cleanPair.append((user, u2))
+        for u in cleanList:
+            del self.sao.followees[u]
+
+        for pair in cleanPair:
+            if self.sao.followees.has_key(pair[0]):
+                del self.sao.followees[pair[0]][pair[1]]
+        li = self.sao.followees.keys()
+        for u in li:
+            if len(self.sao.followees[u]) <= 1:
+                del self.sao.followees[u]
+        print 'Kind Note: This method will probably take much time.'
+        # build U-F-NET
+        print 'Building weighted user-friend network...'
+        # filter isolated nodes and low ratings
+        # Definition of Meta-Path
+        p1 = 'UIU'
+        p2 = 'UFU'
+        p3 = 'UFIU'
+        p4 = ''
+        mPaths = [p1, p2, p3]
+
+        self.G = np.random.rand(self.dao.trainingSize()[1], self.walkDim) / 10
+        self.W = np.random.rand(self.dao.trainingSize()[0], self.walkDim) / 10
+
+        self.fItems = {}  # filtered item set
+        for item in self.dao.trainSet_i:
+            if len(self.dao.trainSet_i[item]) > 1:
+                self.fItems[item] = self.dao.trainSet_i[item].keys()
+
+        self.fBuying = {}  # filtered buying set
+        for user in self.dao.trainSet_u:
+            self.fBuying[user] = []
+            for item in self.dao.trainSet_u[user]:
+                if self.fItems.has_key(item) and self.dao.trainSet_u[user][item] > 0.75:
+                    self.fBuying[user].append(item)
+            if self.fBuying[user] == []:
+                del self.fBuying[user]
+        # self.filteredRatings = defaultdict(list)
+        # for item in self.fItems:
+        #     for user in self.fItems[item]:
+        #         if self.fItems[item][user]>0.75:
+        #             self.filteredRatings[user].append(item)
+
+        self.UFNet = defaultdict(list)
+
+        for user1 in self.sao.followees:
+            s1 = set(self.sao.followees[user1])
+            for user2 in self.sao.followees[user1]:
+                if self.sao.followees.has_key(user2):
+                    if user1 <> user2:
+                        s2 = set(self.sao.followees[user2])
+                        weight = len(s1.intersection(s2))
+                        self.UFNet[user1] += [user2] * (weight + 1)
+        #
+        #
+        #
+        #
+        # print 'Generating random meta-path random walks...'
+        # self.walks = []
+        # # self.visited = defaultdict(dict)
+        # for user in self.fBuying:
+        #
+        #
+        #     for mp in mPaths:
+        #         if mp==p1:
+        #             self.walkCount = 5
+        #         if mp==p2:
+        #             self.walkCount = 8
+        #         if mp==p3:
+        #             self.walkCount = 20
+        #         for t in range(self.walkCount):
+        #
+        #             path = [(user, 'U')]
+        #             lastNode = user
+        #             nextNode = user
+        #             lastType = 'U'
+        #             for i in range(self.walkLength / len(mp)):
+        #
+        #                     for tp in mp[1:]:
+        #                         try:
+        #                             if tp == 'I':
+        #                                 # if not self.fBuying.has_key(lastNode):
+        #                                 #     path = []
+        #                                 #     break
+        #                                 nextNode = choice(self.fBuying[lastNode])
+        #
+        #                             if tp == 'U':
+        #                                 # if lastType=='':
+        #                                 #     nextNode = user
+        #                                 if lastType == 'I':
+        #                                     nextNode = choice(self.fItems[lastNode])
+        #                                 elif lastType == 'F':
+        #                                     nextNode = choice(self.UFNet[lastNode])
+        #                                     while not self.dao.user.has_key(nextNode):
+        #                                         nextNode = choice(self.UFNet[lastNode])
+        #
+        #                             if tp == 'F':
+        #                                 # if not self.UFNet.has_key(lastNode):
+        #                                 #     path = []
+        #                                 #     break
+        #                                 nextNode = choice(self.UFNet[lastNode])
+        #                                 while not self.dao.user.has_key(nextNode):
+        #                                     nextNode = choice(self.UFNet[lastNode])
+        #
+        #                             path.append((nextNode, tp))
+        #                             lastNode = nextNode
+        #                             lastType = tp
+        #
+        #                         except (KeyError, IndexError):
+        #                             path = []
+        #                             break
+        #
+        #
+        #
+        #             if path:
+        #                 self.walks.append(path)
+        #                 # print path
+        #                 # if mp == 'UFIU':
+        #                 # pass
+        # shuffle(self.walks)
+        # print 'walks:',len(self.walks)
+        # # Training get top-k friends
+        # print 'Generating user embedding...'
+        # iteration = 1
+        # userList = self.dao.user.keys()
+        # itemList = self.dao.item.keys()
+        # while iteration <= self.epoch:
+        #     loss = 0
+        #
+        #     for walk in self.walks:
+        #         for i, node in enumerate(walk):
+        #             neighbors = walk[max(0, i - self.winSize / 2):min(len(walk) - 1, i + self.winSize / 2)]
+        #             center, ctp = walk[i]
+        #             if ctp == 'U' or ctp == 'F':  # user
+        #                 centerVec = self.W[self.dao.user[center]]
+        #             else:  # Item
+        #                 centerVec = self.G[self.dao.item[center]]
+        #             for entity, tp in neighbors:
+        #                 # negSamples = []
+        #                 currentVec = ''
+        #                 if tp == 'U' or tp == 'F' and center <> entity:
+        #                     currentVec = self.W[self.dao.user[entity]]
+        #                     self.W[self.dao.user[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(userList)
+        #                         while sample == entity:
+        #                             sample = choice(userList)
+        #                         sampleVec = self.W[self.dao.user[sample]]
+        #                         self.W[self.dao.user[sample]] -=   self.rate * (
+        #                             1 - sigmoid(-sampleVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -  log(sigmoid(-sampleVec.dot(centerVec)))
+        #                         # negSamples.append(choice)
+        #                 elif tp == 'I' and center <> entity:
+        #                     currentVec = self.G[self.dao.item[entity]]
+        #                     self.G[self.dao.item[entity]] +=   self.rate * (
+        #                         1 - sigmoid(currentVec.dot(centerVec))) * centerVec
+        #                     if ctp == 'U' or ctp == 'F':
+        #                         self.W[self.dao.user[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     else:
+        #                         self.G[self.dao.item[center]] +=   self.rate * (
+        #                             1 - sigmoid(currentVec.dot(centerVec))) * currentVec
+        #                     loss += -  log(sigmoid(currentVec.dot(centerVec)))
+        #                     for i in range(self.neg):
+        #                         sample = choice(itemList)
+        #                         while sample == entity:
+        #                             sample = choice(itemList)
+        #                         # negSamples.append(choice)
+        #                         sampleVec = self.G[self.dao.item[sample]]
+        #                         self.G[self.dao.item[sample]] -= self.rate * (
+        #                             1 - sigmoid(-currentVec.dot(centerVec))) * centerVec
+        #                         if ctp == 'U' or ctp == 'F':
+        #                             self.W[self.dao.user[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         else:
+        #                             self.G[self.dao.item[center]] -=   self.rate * (
+        #                                 1 - sigmoid(-sampleVec.dot(centerVec))) * sampleVec
+        #                         #loss += -self.alpha * log(sigmoid(-sampleVec.dot(centerVec)))
+        #     shuffle(self.walks)
+        #
+        #     print 'iteration:', iteration, 'loss:', loss
+        #     iteration += 1
+        # print 'User embedding generated.'
+        # from mpl_toolkits.mplot3d import Axes3D
+        # from sklearn.decomposition import PCA
+        # pca = PCA(n_components=3)
+        # dW = pca.fit_transform(self.W)
+        # dG = pca.fit_transform(self.G)
+        # ax = plt.figure().add_subplot(111, projection='3d')
+        # #print len(self.W[:,0])
+        # x = []
+        # y = []
+        # z = []
+        # from random import random
+        # for user in self.fBuying:
+        #
+        #     x.append(dW[self.dao.user[user]][0])
+        #     y.append(dW[self.dao.user[user]][1])
+        #     z.append(dW[self.dao.user[user]][2])
+        # print len(x)
+        # ax.scatter(x,y,z,marker='*',color='red')
+        # x = []
+        # y = []
+        # z = []
+        # for item in self.fItems:
+        #     x.append(dG[self.dao.item[item]][0])
+        #     y.append(dG[self.dao.item[item]][1])
+        #     z.append(dG[self.dao.item[item]][2])
+        # print len(x)
+        # ax.scatter(x, y,z, marker='o', color='green')
+        # plt.show()
+        #
+        # print 'Constructing similarity matrix...'
+        # i = 0
+        # self.topKSim = {}
+        # self.Sim = SymmetricMatrix(len(self.UFNet))
+        # for user1 in self.fBuying:
+        #     uSim = []
+        #     i+=1
+        #     if i%200==0:
+        #         print i,'/',len(self.fBuying)
+        #     vec1 = self.W[self.dao.user[user1]]
+        #     for user2 in self.fBuying:
+        #         if user1 <> user2:
+        #             vec2 = self.W[self.dao.user[user2]]
+        #             sim = cosine(vec1, vec2)
+        #             uSim.append((user2,sim))
+        #
+        #     self.topKSim[user1] = sorted(uSim, key=lambda d: d[1], reverse=True)[:self.topK]
+        #
+        #
+        # print 'Similarity matrix finished.'
+        # #print self.topKSim
+        import pickle
+
+        # recordTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
+        # similarity = open('sim'+recordTime+'.pkl', 'wb')
+        # vectors = open('vec'+recordTime+'.pkl', 'wb')
+        # Pickle dictionary using protocol 0.
+
+        # pickle.dump(self.topKSim, similarity)
+        # pickle.dump((self.W,self.G),vectors)
+        # similarity.close()
+        # vectors.close()
+
+        # matrix decomposition
+        pkl_file = open('MPE-Ciao-sim'+self.foldInfo+'.pkl', 'rb')
+
+        self.topKSim = pickle.load(pkl_file)
+        print 'Decomposing...'
+        self.F = np.random.rand(self.dao.trainingSize()[0], self.k) / 10
+
+        iteration = 0
+        while iteration < self.maxIter:
+            self.loss = 0
+
+            for user in self.fBuying:
+                u = self.dao.user[user]
+                friends = self.topKSim[user][:self.topK]
+                for friend in friends:
+                    uf = self.dao.user[friend[0]]
+                    #self.P[u] -= self.lRate * (self.P[u] - self.P[uf]) * self.alpha
+                    #self.loss += self.alpha * (self.P[u] - self.P[uf]).dot(self.P[u] - self.P[uf])
+                    error = friend[1] - self.P[u].dot(self.F[uf])
+                    self.loss += self.alpha*error ** 2
+                    p = self.P[u]
+                    z = self.F[uf]
+
+                    # update latent vectors
+                    self.P[u] += self.alpha* self.lRate * (error * z )  # - self.alpha * (self.P[u]-self.W[u]))
+                    self.F[uf] += self.alpha* self.lRate * (error * p -self.regU*z)
+                    #self.W[uf] += self.alpha* self.lRate * (error * p - self.regI * q)
+
+
+            for entry in self.dao.trainingData:
+                user, item, rating = entry
+                u = self.dao.user[user]  # get user id
+                i = self.dao.item[item]  # get item id
+                error = rating - self.P[u].dot(self.Q[i])
+                self.loss += error ** 2
+                p = self.P[u]
+                q = self.Q[i]
+
+                # update latent vectors
+                self.P[u] += self.lRate * (error * q - self.regU * p)  # - self.alpha * (self.P[u]-self.W[u]))
+                self.Q[i] += self.lRate * (error * p - self.regI * q)
+
+
+            self.loss += self.regU * (self.P * self.P).sum() + self.regI * (self.Q * self.Q).sum()
+            iteration += 1
+            if self.isConverged(iteration):
+                break
diff --git a/baseclass/IterativeRecommender.py b/baseclass/IterativeRecommender.py
index 49881cb0..866ddfe7 100644
--- a/baseclass/IterativeRecommender.py
+++ b/baseclass/IterativeRecommender.py
@@ -135,7 +135,7 @@ def ranking_performance(self):
         recList = {}
         testSample = {}
         for user in self.dao.testSet_u:
-            if len(testSample) == 300:
+            if len(testSample) == 1000:
                 break
             testSample[user] = self.dao.testSet_u[user]
 
@@ -183,10 +183,10 @@ def ranking_performance(self):
             recList[user] = zip(resNames, recommendations)
         measure = Measure.rankingMeasure(testSample, recList, [10])
         print '-'*80
-        print 'Ranking Performance '+self.foldInfo+' (Top-10 On 300 sampled users)'
+        print 'Ranking Performance '+self.foldInfo+' (Top-10 On 1000 sampled users)'
         for m in measure[1:]:
             print m.strip()
         print '-'*80
-        #self.record.append(measure[3].strip()+' '+measure[4])
+        self.record.append(measure[3].strip()+' '+measure[4])
         return measure
 
diff --git a/baseclass/Recommender.py b/baseclass/Recommender.py
index 5f6e68d7..124f212d 100644
--- a/baseclass/Recommender.py
+++ b/baseclass/Recommender.py
@@ -259,8 +259,8 @@ def execute(self):
         if self.isSaveModel:
             print 'Saving model %s...' %(self.foldInfo)
             self.saveModel()
-        # with open(self.foldInfo+'measure.txt','w') as f:
-        #     f.writelines(self.record)
+        with open(self.foldInfo+self.config['recommender']+' measure.txt','w') as f:
+            f.writelines(self.record)
         return self.measure
 
 
diff --git a/config/BPR.conf b/config/BPR.conf
index 2bcdf387..6b7d8035 100644
--- a/config/BPR.conf
+++ b/config/BPR.conf
@@ -1,10 +1,10 @@
-ratings=../dataset/lastfm/ratings.txt
-ratings.setup=-columns 0 1 2
+ratings=../dataset/nowplaying/log2.txt
+ratings.setup=-columns 1 2 4 -delim ,
 recommender=BPR
-evaluation.setup=-ap 0.2 -b 1
+evaluation.setup=-cv 5 -b 1
 item.ranking=on -topN 5,10
 num.factors=20
-num.max.iter=10
-learnRate=-init 0.05 -max 1
+num.max.iter=20
+learnRate=-init 0.02 -max 1
 reg.lambda=-u 0.01 -i 0.01 -b 0.2 -s 0.2
 output.setup=on -dir ../results/
\ No newline at end of file
diff --git a/config/CUNE_MF.conf b/config/CUNE_MF.conf
index ac63270e..dfa257ef 100644
--- a/config/CUNE_MF.conf
+++ b/config/CUNE_MF.conf
@@ -3,9 +3,9 @@ ratings.setup=-columns 0 1 2
 recommender=CUNE_MF
 evaluation.setup=-testSet ../dataset/FilmTrust/testset.txt
 item.ranking=off -topN 10
-num.factors=10
-num.max.iter=30
-CUNE-MF=-T 10 -L 5 -l 10 -w 5 -k 5 -a 0.5 -ep 10
+num.factors=20
+num.max.iter=50
+CUNE-MF=-T 15 -L 20 -l 20 -w 5 -k 20 -a 0.1 -ep 5 -r 0.2
 learnRate=-init 0.05 -max 0.1
 reg.lambda=-u 0.01 -i 0.01 -b 0.01 -s 0.2
 output.setup=on -dir ../results/
\ No newline at end of file
diff --git a/config/ExpoMF.conf b/config/ExpoMF.conf
index 90273218..3b60dfdf 100644
--- a/config/ExpoMF.conf
+++ b/config/ExpoMF.conf
@@ -1,10 +1,10 @@
-ratings=../dataset/epinions/sratings.txt
-ratings.setup=-columns 0 1 2
+ratings=../dataset/nowplaying/log2.txt
+ratings.setup=-columns 1 2 4 -delim ,
 recommender=ExpoMF
 evaluation.setup=-cv 5 -b 1
 item.ranking=on -topN 10
 num.factors=20
-num.max.iter=15
+num.max.iter=5
 learnRate=-init 0.01 -max 1
 reg.lambda=-u 1 -i 0.02 -b 0.02
 output.setup=on -dir ../results/
diff --git a/config/FM_BPR.conf b/config/FM_BPR.conf
new file mode 100644
index 00000000..be23dc20
--- /dev/null
+++ b/config/FM_BPR.conf
@@ -0,0 +1,13 @@
+ratings=../dataset/lastfm/ratings.txt
+social=../dataset/lastfm/trusts.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1
+recommender=FM_BPR
+evaluation.setup=-ap 0.2 -b 1
+item.ranking=on -topN 10,20
+num.factors=20
+num.max.iter=30
+learnRate=-init 0.05 -max 1
+FM_BPR=-f 0.5 -m 0.5
+reg.lambda=-u 0.01 -i 0.01 -b 0.2 -s 0.2
+output.setup=on -dir ../results/
\ No newline at end of file
diff --git a/config/HER.conf b/config/HER.conf
new file mode 100644
index 00000000..7ec02c33
--- /dev/null
+++ b/config/HER.conf
@@ -0,0 +1,13 @@
+ratings=../dataset/lastfm/ratings.txt
+social=../dataset/lastfm/trusts.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1
+recommender=HER
+evaluation.setup=-ap 0.2
+item.ranking=on -topN 10
+num.factors=10
+num.max.iter=1
+HER=-T 2 -L 20 -l 20 -w 5 -k 5 -a 0 -ep 5 -neg 5 -r 0.03
+learnRate=-init 0.05 -max 1
+reg.lambda=-u 0.01 -i 0.01 -b 0.01 -s 0.2
+output.setup=on -dir ../results/
diff --git a/config/HERP.conf b/config/HERP.conf
new file mode 100644
index 00000000..27c7418e
--- /dev/null
+++ b/config/HERP.conf
@@ -0,0 +1,13 @@
+ratings=../dataset/lastfm/sratings.txt
+social=../dataset/lastfm/trusts.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1
+recommender=HERP
+evaluation.setup=-ap 0.2
+item.ranking=on -topN 10
+num.factors=20
+num.max.iter=30
+HERP=-T 2 -L 20 -l 20 -w 5 -k 50 -a 0 -ep 5 -neg 5 -r 0.03
+learnRate=-init 0.05 -max 1
+reg.lambda=-u 0.01 -i 0.01 -b 0.01 -s 0.2
+output.setup=on -dir ../results/
diff --git a/config/MERec.conf b/config/MERec.conf
new file mode 100644
index 00000000..1c812e3b
--- /dev/null
+++ b/config/MERec.conf
@@ -0,0 +1,11 @@
+ratings=../dataset/nowplaying/log2.txt
+ratings.setup=-columns 1 2 4 -delim ,
+recommender=MERec
+evaluation.setup=-cv 5 -b 1
+item.ranking=on -topN 10
+num.factors=20
+num.max.iter=5
+learnRate=-init 0.01 -max 1
+reg.lambda=-u 1 -i 0.02 -b 0.02 -s 0.01
+output.setup=on -dir ../results/
+
diff --git a/config/MPE_BPR.conf b/config/MPE_BPR.conf
new file mode 100644
index 00000000..93f66c38
--- /dev/null
+++ b/config/MPE_BPR.conf
@@ -0,0 +1,13 @@
+ratings=../dataset/lastfm/ratings.txt
+social=../dataset/lastfm/trusts.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1
+recommender=MPE_BPR
+evaluation.setup=-cv 5 -b 3.0
+item.ranking=on -topN 10
+num.factors=10
+num.max.iter=1
+CUNE-MF=-T 2 -L 20 -l 20 -w 5 -k 20 -a 0.2 -ep 5 -neg 5 -r 0.03
+learnRate=-init 0.03 -max 1
+reg.lambda=-u 0.01 -i 0.01 -b 0.01 -s 0.2
+output.setup=on -dir ../results/
diff --git a/config/MPE_MF.conf b/config/MPE_MF.conf
new file mode 100644
index 00000000..2e123697
--- /dev/null
+++ b/config/MPE_MF.conf
@@ -0,0 +1,13 @@
+ratings=../dataset/filmtrust/trainset.txt
+social=../dataset/filmtrust/trust.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1 2
+recommender=MPE_MF
+evaluation.setup=-testSet ../dataset/filmtrust/testset.txt
+item.ranking=off -topN 10
+num.factors=10
+num.max.iter=50
+CUNE-MF=-T 2 -L 20 -l 20 -w 5 -k 20 -a 0.3 -ep 3 -neg 5 -r 0.05
+learnRate=-init 0.03 -max 1
+reg.lambda=-u 0.01 -i 0.01 -b 0.01 -s 0.2
+output.setup=on -dir ../results/
diff --git a/config/NPR.conf b/config/NPR.conf
new file mode 100644
index 00000000..31ea9a70
--- /dev/null
+++ b/config/NPR.conf
@@ -0,0 +1,14 @@
+ratings=../dataset/epinions/sratings.txt
+social=../dataset/epinions/strusts.txt
+ratings.setup=-columns 0 1 2
+social.setup=-columns 0 1 2
+recommender=NPR
+evaluation.setup=-cv 5 -b 1
+item.ranking=on -topN 5,10
+num.factors=64
+num.max.iter=500
+batch_size=512
+NPR=-regA 2 -eps 0.5 -advEpoch 500
+learnRate=-init 0.003 -max 1
+reg.lambda=-u 0.002 -i 0.01 -b 0.2 -s 0.2
+output.setup=on -dir ../results/
diff --git a/config/PaperRec b/config/PaperRec
new file mode 100644
index 00000000..e69de29b
diff --git a/config/SocialMF.conf b/config/SocialMF.conf
index 760fe4ee..6e794005 100644
--- a/config/SocialMF.conf
+++ b/config/SocialMF.conf
@@ -5,8 +5,8 @@ social.setup=-columns 0 1 2
 recommender=SocialMF
 evaluation.setup=-testSet ../dataset/FilmTrust/testset.txt
 item.ranking=off -topN 30
-num.factors=5
-num.max.iter=30
-learnRate=-init 0.05 -max 1
-reg.lambda=-u 0.05 -i 0.05 -b 0.1 -s 0.1
+num.factors=10
+num.max.iter=50
+learnRate=-init 0.03 -max 1
+reg.lambda=-u 0.01 -i 0.01 -b 0.1 -s 0.5
 output.setup=on -dir ../results/
\ No newline at end of file
diff --git a/config/WRMF.conf b/config/WRMF.conf
index 9938eb87..3690debe 100644
--- a/config/WRMF.conf
+++ b/config/WRMF.conf
@@ -1,10 +1,10 @@
-ratings=../dataset/lastfm/sratings.txt
-ratings.setup=-columns 0 1 2
+ratings=../dataset/nowplaying/log2.txt
+ratings.setup=-columns 1 2 4 -delim ,
 recommender=WRMF
 evaluation.setup=-cv 5 -b 1
 item.ranking=on -topN 10
 num.factors=20
-num.max.iter=7
+num.max.iter=5
 WRMF=-alpha 10 -lamba 1
 learnRate=-init 0.01 -max 1
 reg.lambda=-u 1 -i 0.02 -b 0.02
diff --git a/main/main.py b/main/main.py
index e7a2ae9a..75fe6ee5 100644
--- a/main/main.py
+++ b/main/main.py
@@ -16,7 +16,7 @@
     print 'Rating-based Recommenders:'
     print '1. UserKNN        2. ItemKNN        3. BasicMF        4. SlopeOne        5. SVD'
     print '6. PMF            7. SVD++          8. EE             9. BPR             10. WRMF'
-    print '11. ExpoMF'
+    print '11. ExpoMF        12. MERec'
 
     print 'Social Recommenders:'
     print 's1. RSTE          s2. SoRec         s3. SoReg         s4. SocialMF       s5. SBPR'
@@ -25,7 +25,7 @@
 
     print 'Advanced Recommenders:'
     print 'a1. CoFactor      a2. CUNE-MF       a3. CUNE-BPR      a4. IF-BPR         a5. APR'
-
+    print 'a6. NPR'
     print 'Baselines:'
     print 'b1. UserMean      b2. ItemMean      b3. MostPopular   b4. Rand'
 
@@ -80,6 +80,9 @@
     elif order == '11':
         conf = Config('../config/ExpoMF.conf')
 
+    elif order == '12':
+        conf = Config('../config/MERec.conf')
+
     elif order == 's1':
         conf = Config('../config/RSTE.conf')
 
@@ -125,6 +128,9 @@
     elif order == 'a5':
         conf = Config('../config/APR.conf')
 
+    elif order == 'a6':
+        conf = Config('../config/NPR.conf')
+
     elif order == 'b1':
         conf = Config('../config/UserMean.conf')