实现的基于user和item的协同过滤算法+源代码+文档说明

#!/usr/bin/python # -*- coding: utf-8 -*- ''' @desc 基于用户的协同过滤算法,方法为User-IIF @author cheng.cheng @email [email protected] @date 2012-06-18 ''' import sys import random import math from operator import itemgetter def ReadData(file,data): ''' 读取评分数据 @param file 评分数据文件 @param data 储存评分数据的List ''' for line in file: line = line.strip('\n') linelist = line.split("::") data.append([linelist[0],linelist[1]]) def SplitData(data, M, key, seed): ''' 将数据分为训练集和测试集 @param data 储存训练和测试数据的List @param M 将数据分为M份 @param key 选取第key份数据做为测试数据 @param seed 随机种子 @return train 训练数据集Dict @return test 测试数据集Dict ''' test = dict () train = dict () random.seed(seed) for user,item in data: if random.randint(0,M) == key: if user in test: test[user].append(item) else: test[user] = [] else: if user in train: train[user].append(item) else: train[user] = [] return train, test def UserSimilarity(train): ''' 计算用户相似度 @param train 训练数据集Dict @return W 记录用户相似度的二维矩阵 ''' #建立物品到用户之间的倒查表，降低计算用户相似度的时间复杂性 item_users = dict() for u, items in train.items(): for i in items: if(i not in item_users): item_users[i] = set() item_users[i].add(u) C = dict() N = dict() #计算用户之间共有的item的数目 for i, users in item_users.items(): for u in users: if(u not in N): N[u] = 1 N[u] += 1 for v in users: if u == v: continue if(u not in C): C[u] = dict() if(v not in C[u]): C[u][v] = 0 #对热门物品进行了惩罚，采用这种方法被称做UserCF-IIF C[u][v] += (1 / math.log(1+len(users))) W = dict() for u, related_users in C.items(): for v, cuv in related_users.items(): if(u not in W): W[u] = dict() #利用余弦相似度计算用户之间的相似度 W[u][v] = cuv / math.sqrt(N[u] * N[v]) return W def Coverage(train, test, W, N, K): ''' 获取推荐结果 @param user 输入的用户 @param train 训练数据集Dict @param W 记录用户相似度的二维矩阵 @param N 推荐结果的数目 @param K 选取近邻的数目 ''' recommned_items = set() all_items = set() for user in train.keys(): for item in train[user]: all_items.add(item) rank = GetRecommendation(user, train, W, N, K) for item, pui in rank: recommned_items.add(item) #print 'len: ',len(recommned_items),'\n' return len(recommned_items) / (len(all_items) * 1.0) def GetRecommendation(user, train ,W, N, K): ''' 获取推荐结果 @param user 输入的用户 @param train 训练数据集Dict @param W 记录用户相似度的二维矩阵 @param N 推荐结果的数目 @param K 选取近邻的数目 ''' rank = dict() interacted_items = train[user] #选取K个近邻计算得分 for v,wuv in sorted(W[user].items(), key=itemgetter(1),\ reverse = True)[0:K]: for i in train[v]: if i in interacted_items: continue if i in rank: rank[i] += wuv else: rank[i] = 0 #取得分最高的N个item作为推荐结果 rank = sorted(rank.items(), key=itemgetter(1), reverse = True)[0:N] return rank def Recall(train, test, W, N, K): ''' 计算推荐结果的召回率 @param train 训练数据集Dict @param test 测试数据集Dict @param W 记录用户相似度的二维矩阵 @param N 推荐结果的数目 @param K 选取近邻的数目 ''' hit = 0 all = 0 for user in train.keys(): if user in test: tu = test[user] rank = GetRecommendation(user, train, W, N, K) for item, pui in rank: if item in tu: hit+= 1 all += len(tu) #print(hit) #print(all) return hit/(all * 1.0) def Precision(train, test, W, N, K): ''' 计算推荐结果的准确率 @param train 训练数据集Dict @param test 测试数据集Dict @param W 记录用户相似度的二维矩阵 @param N 推荐结果的数目 @param K 选取近邻的数目 ''' hit = 0 all = 0 for user in train.keys(): if user in test: tu = test[user] rank = GetRecommendation(user, train, W, N, K) for item, pui in rank: if item in tu: hit+= 1 all += N #print(hit) #print(all) return hit/(all * 1.0) def Popularity(train, test, W, N, K): ''' 计算推荐结果的流行度 @param train 训练数据集Dict @param test 测试数据集Dict @param W 记录用户相似度的二维矩阵 @param N 推荐结果的数目 @param K 选取近邻的数目 ''' item_popularity = dict() for user, items in train.items(): for item in items: if item not in item_popularity: item_popularity[item] = 0 item_popularity[item] += 1 ret = 0 n = 0 for user in train.keys(): rank = GetRecommendation(user, train, W, N, K) for item, pui in rank: ret += math.log(1+ item_popularity[item]) n += 1 ret /= n * 1.0 return ret if __name__ == '__main__': data = [] M = 8 key = 1 seed = 1 N = 10 K = 80 W = dict() rank = dict() print("this is the main function") file = open('./ml-1m/rat.dat') ReadData(file,data) train,test = SplitData(data, M, key, seed) W = UserSimilarity(train) recall = Recall(train, test, W, N, K) precision = Precision(train, test, W, N, K) popularity = Popularity(train, test, W, N, K) coverage = Coverage(train, test, W, N, K) print 'recall: ',recall,'\n' print 'precision: ',precision,'\n' print 'Popularity: ',popularity,'\n' print 'coverage: ', coverage,'\n' else : print("this is not the main function")