基于用户的协同过滤算法，用户协同过滤算法,本代码是在学习《推荐系统

基于用户的协同过滤算法，用户协同过滤算法,本代码是在学习《推荐系统

本代码是在学习《推荐系统实践》一书时完成的，参考了原书作者项亮的算法完成了程序，并且可运行和测试，本部分是基于用户的协同过滤算法的实现，基本与书上结论一致，希望对于学习推荐的同学有帮助。

import randomimport mathclass UserBasedCF:    def __init__(self,datafile = None):        self.datafile = datafile        self.readData()        self.splitData(3,47)    def readData(self,datafile = None):        """        read the data from the data file which is a data set        """        self.datafile = datafile or self.datafile        self.data = []        for line in open(self.datafile):            userid,itemid,record,_ = line.split()            self.data.append((userid,itemid,int(record)))    def splitData(self,k,seed,data=None,M = 8):        """        split the data set        testdata is a test data set        traindata is a train set         test data set / train data set is 1:M-1        """        self.testdata = {}        self.traindata = {}        data = data or self.data        random.seed(seed)        for user,item, record in self.data:            if random.randint(0,M) == k:                self.testdata.setdefault(user,{})                self.testdata[user][item] = record             else:                self.traindata.setdefault(user,{})                self.traindata[user][item] = record    def userSimilarity(self,train = None):        """        One method of getting user similarity matrix        """        train = train or self.traindata        self.userSim = dict()        for u in train.keys():            for v in train.keys():                if u == v:                    continue                self.userSim.setdefault(u,{})                self.userSim[u][v] = len(set(train[u].keys()) & set(train[v].keys()))                self.userSim[u][v] /=math.sqrt(len(train[u]) * len(train[v]) *1.0)    def userSimilarityBest(self,train = None):        """        the other method of getting user similarity which is better than above        you can get the method on page 46        In this experiment，we use this method        """        train = train or self.traindata        self.userSimBest = dict()        item_users = dict()        for u,item in train.items():            for i in item.keys():                item_users.setdefault(i,set())                item_users[i].add(u)        user_item_count = dict()        count = dict()        for item,users in item_users.items():            for u in users:                user_item_count.setdefault(u,0)                user_item_count[u] += 1                for v in users:                    if u == v:continue                    count.setdefault(u,{})                    count[u].setdefault(v,0)                    count[u][v] += 1        for u ,related_users in count.items():            self.userSimBest.setdefault(u,dict())            for v, cuv in related_users.items():                self.userSimBest[u][v] = cuv / math.sqrt(user_item_count[u] * user_item_count[v] * 1.0)    def recommend(self,user,train = None,k = 8,nitem = 40):        train = train or self.traindata        rank = dict()        interacted_items = train.get(user,{})        for v ,wuv in sorted(self.userSimBest[user].items(),key = lambda x : x[1],reverse = True)[0:k]:            for i , rvi in train[v].items():                if i in interacted_items:                    continue                rank.setdefault(i,0)                rank[i] += wuv        return dict(sorted(rank.items(),key = lambda x :x[1],reverse = True)[0:nitem])    def recallAndPrecision(self,train = None,test = None,k = 8,nitem = 10):        """        Get the recall and precision, the method you want to know is listed         in the page 43        """        train  = train or self.traindata        test = test or self.testdata        hit = 0        recall = 0        precision = 0        for user in train.keys():            tu = test.get(user,{})            rank = self.recommend(user, train = train,k = k,nitem = nitem)             for item,_ in rank.items():                if item in tu:                    hit += 1            recall += len(tu)            precision += nitem        return (hit / (recall * 1.0),hit / (precision * 1.0))    def coverage(self,train = None,test = None,k = 8,nitem = 10):        train = train or self.traindata        test = test or self.testdata        recommend_items = set()        all_items  = set()        for user in train.keys():            for item in train[user].keys():                all_items.add(item)            rank = self.recommend(user, train, k = k, nitem = nitem)            for item,_ in rank.items():                recommend_items.add(item)        return len(recommend_items) / (len(all_items) * 1.0)    def popularity(self,train = None,test = None,k = 8,nitem = 10):        """        Get the popularity        the algorithm on page 44        """        train = train or self.traindata        test = test or self.testdata        item_popularity = dict()        for user ,items in train.items():            for item in items.keys():                item_popularity.setdefault(item,0)                item_popularity[item] += 1        ret = 0        n = 0        for user in train.keys():            rank = self.recommend(user, train, k = k, nitem = nitem)            for item ,_ in rank.items():                ret += math.log(1+item_popularity[item])                n += 1        return ret / (n * 1.0)def testRecommend():    ubcf = UserBasedCF('u.data')    ubcf.readData()    ubcf.splitData(4,100)    ubcf.userSimilarity()    user = "345"    rank = ubcf.recommend(user,k = 3)    for i,rvi in rank.items():        items = ubcf.testdata.get(user,{})        record = items.get(i,0)        print "%5s: %.4f--%.4f" %(i,rvi,record)def testUserBasedCF():    cf  =  UserBasedCF('u.data')    cf.userSimilarityBest()    print "%3s%20s%20s%20s%20s" % ('K',"recall",'precision','coverage','popularity')    for k in [5,10,20,40,80,160]:        recall,precision = cf.recallAndPrecision( k = k)        coverage = cf.coverage(k = k)        popularity = cf.popularity(k = k)        print "%3d%19.3f%%%19.3f%%%19.3f%%%20.3f" % (k,recall * 100,precision * 100,coverage * 100,popularity)if __name__ == "__main__":    testUserBasedCF()#该片段来自于http://byrx.net