基于物品的协同过滤算法源码及数据集Python

# -*- coding: utf-8 -*- """ Created on Mon Jun 4 09:41:15 2018 @author: Administrator """ # -*-coding=utf-8 -*- import math from texttable import Texttable import imp #计算余弦距离 def getCosDist(user1, user2): sum_x = 0.0 sum_y = 0.0 sum_xy = 0.0 for key1 in user1: for key2 in user2: if key1[0] == key2[0]: sum_x += key1[1] * key1[1] sum_y += key2[1] * key2[1] sum_xy += key1[1] * key2[1] if sum_xy == 0.0: return 0 demo = math.sqrt(sum_x * sum_y) return sum_xy / demo #读取文件，读取以行为单位，每一行是列表里的一个元素 def readFile(filename): contents = [] f = open(filename, "r",encoding = "ISO-8859-1") contents = f.readlines() f.close() return contents #数据格式化为二维数组 def getRatingInfo(ratings): rates = [] for line in ratings: rate = line.split("\t") rates.append([int(rate[0]), int(rate[1]), int(rate[2])]) return rates #生成用户评分数据结构 def getUserScoreDataStructure(rates): #userDict[2]=[(1,5),(4,2)].... 表示用户2对电影1的评分是5，对电影4的评分是2 userDict = {} itemUser = {} for k in rates: user_rank = (k[1], k[2]) if k[0] in userDict: userDict[k[0]].append(user_rank) else: userDict[k[0]] = [user_rank] if k[1] in itemUser: itemUser[k[1]].append(k[0]) else: itemUser[k[1]] = [k[0]] return userDict, itemUser #计算与指定用户最相近的邻居 def getNearestNeighbor(userId, userDict, itemUser): neighbors = [] for item in userDict[userId]: for neighbor in itemUser[item[0]]: if neighbor != userId and neighbor not in neighbors: neighbors.append(neighbor) neighbors_dist = [] for neighbor in neighbors: dist = getCosDist(userDict[userId], userDict[neighbor]) neighbors_dist.append([dist, neighbor]) neighbors_dist.sort(reverse = True) return neighbors_dist #使用UserFC进行推荐，输入：文件名,用户ID,邻居数量 def recommendByUserFC(filename, userId, k = 5): #读取文件 contents = readFile(filename) #文件格式数据转化为二维数组 rates = getRatingInfo(contents) #格式化成字典数据 userDict, itemUser = getUserScoreDataStructure(rates) #找邻居 neighbors = getNearestNeighbor(userId, userDict, itemUser)[:5] #建立推荐字典 recommand_dict = {} for neighbor in neighbors: neighbor_user_id = neighbor[1] movies = userDict[neighbor_user_id] for movie in movies: if movie[0] not in recommand_dict: recommand_dict[movie[0]] = neighbor[0] else: recommand_dict[movie[0]] += neighbor[0] #建立推荐列表 recommand_list = [] for key in recommand_dict: recommand_list.append([recommand_dict[key], key]) recommand_list.sort(reverse = True) user_movies = [k[0] for k in userDict[userId]] return [k[1] for k in recommand_list], user_movies, itemUser, neighbors #获取电影的列表 def getMovieList(filename): contents = readFile(filename) movies_info = {} for movie in contents: single_info = movie.split("|") movies_info[int(single_info[0])] = single_info[1:] return movies_info #从这里开始运行 if __name__ == '__main__': #获取所有电影的列表 movies = getMovieList("u.item") recommend_list, user_movie, items_movie, neighbors = recommendByUserFC("u.data", 5, 100) neighbors_id=[ i[1] for i in neighbors] table = Texttable() table.set_deco(Texttable.HEADER) table.set_cols_dtype(['t', 't', 't']) table.set_cols_align(["l", "l", "l"]) rows=[] rows.append([u"movie name",u"release", u"from userid"]) for movie_id in recommend_list[:20]: from_user=[] for user_id in items_movie[movie_id]: if user_id in neighbors_id: from_user.append(user_id) rows.append([movies[movie_id][0],movies[movie_id][1],""]) table.add_rows(rows) print (table.draw())