转自：用户推荐系统_python 代码-豆瓣
书籍：项亮的<推荐系统实践>
import random import math class UserBasedCF: def __init__(self,train = None,test = None): self.trainfile = train self.testfile = test self.readData() def readData(self,train = None,test = None): self.trainfile = train or self.trainfile self.testfile = test or self.testfile self.traindata = {} self.testdata = {} for line in open(self.trainfile): userid,itemid,record,_ = line.split() self.traindata.setdefault(userid,{}) self.traindata[userid][itemid]=record for line in open(self.testfile): userid,itemid,record,_ = line.split() self.testdata.setdefault(userid,{}) self.testdata[userid][itemid]=record def userSimilarityBest(self,train = None): 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]:#获取与user相似度最高的k个用户 for i , rvi in train[v].items(): if i in interacted_items: continue #只选择user没有评分过的物品进行推荐 rank.setdefault(i,0)#设置初始值，以便做下面的累加运算 rank[i] += wuv #书中为rank[i] +=rvi*wuv return dict(sorted(rank.items(),key = lambda x :x[1],reverse = True)[0:nitem])#用sorted方法对推荐的物品进行排序，预计评分高的排在前面，再取其中nitem个，nitem为每个用户推荐的物品数量 def recallAndPrecision(self,train = None,test = None,k = 8,nitem = 10): 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,{})#如果测试集中没有这个用户，则将tu初始化为空，避免test[user]报错 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): 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 testUserBasedCF(): train = 'u1.base' test = 'u1.test' cf = UserBasedCF(train,test) cf.userSimilarityBest() print("%3s%20s%20s%20s%20s" % ('K',"precision",'recall','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,precision * 100,recall * 100,coverage * 100,popularity)) if __name__ == "__main__": testUserBasedCF() 基于项目的推荐系统，IBCF： ''' Created on 2013-10-10 @author: Administrator ''' import random import math class KNN: def __init__(self,train = None,test = None): self.trainfile = train self.testfile = test self.readData() def readData(self,train = None,test = None): self.trainfile = train or self.trainfile self.testfile = test or self.testfile self.traindata = {} self.testdata = {} for line in open(self.trainfile): userid,itemid,record,_ = line.split() self.traindata.setdefault(userid,{}) self.traindata[userid][itemid]=record for line in open(self.testfile): userid,itemid,record,_ = line.split() self.testdata.setdefault(userid,{}) self.testdata[userid][itemid]=record def ItemSim(self,train = None): train = train or self.traindata ItemSimcount = dict() Item_count = dict() for _,items in train.items(): for itemidi in items.keys(): Item_count.setdefault(itemidi,0) Item_count[itemidi] += 1 for itemidj in items.keys(): if itemidi == itemidj: continue ItemSimcount.setdefault(itemidi,{}) ItemSimcount[itemidi].setdefault(itemidj,0) ItemSimcount[itemidi][itemidj] +=1 self.ItemSimlist = dict() for itemidi, related_item in ItemSimcount.items(): self.ItemSimlist.setdefault(itemidi,{}) for itemidj,wij in related_item.items(): self.ItemSimlist[itemidi].setdefault(itemidj,0) self.ItemSimlist[itemidi][itemidj] = wij/math.sqrt(Item_count[itemidi]*Item_count[itemidj]*1.0) def recommend(self,user,train = None,k = 5,nitem = 10): train = train or self.traindata recommendlist = dict() User_Itemlist = train.get(user,{}) for i,ri in User_Itemlist.items(): for j,wij in sorted(self.ItemSimlist[i].items(),key = lambda x:x[1],reverse = True)[0:k]: if j in User_Itemlist: continue recommendlist.setdefault(j,0) recommendlist[j] += float(ri)*wij return dict(sorted(recommendlist.items(),key = lambda x :x[1],reverse = True)[0:nitem]) def recallAndPrecision(self,train = None,test = None,k = 5,nitem = 10): 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 = 5,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 = 5,nitem = 10): 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(): if item in item_popularity: ret += math.log(1+item_popularity[item]) n += 1 return ret / (n * 1.0) def testKNNCF(): train = 'u1.base' test = 'u1.test' cf = KNN(train,test) cf.ItemSim() print("%3s%20s%20s%20s%20s" % ('K',"precision",'recall','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,precision * 100,recall * 100,coverage * 100,popularity)) if __name__ == "__main__": testKNNCF()