协同过滤原理及Python实现

# -*-coding=utf-8 -*-  
import sys  
import math  from texttable import Texttable
#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")  
    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__':  
    reload(sys)  
    sys.setdefaultencoding('utf-8')  
    #获取所有电影的列表  
    movies = getMovieList("u.item")  
    recommend_list, user_movie, items_movie, neighbors = recommendByUserFC("u.data", 50, 80)  
    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()