推荐系统-基于用户的最近邻协同过滤算法（MovieLens数据集）修复版

由于本站的前一个这个算法过早，无人回复，本人运用他的代码出现了许多问题，所以将其修复后再次上传。
本文，用皮尔逊相关系数计算用户之间的相似性。

from operator import itemgetter, attrgetter  
from math import sqrt  
import codecs
  
def load_data():  
      
    filename_user_movie = 'E:/code/ml-100k/u.data'  
    filename_movieInfo = 'E:/code/ml-100k/u.item'  
  
  
    user_movie = {
     }  
    for line in open(filename_user_movie):  
        (userId, itemId, rating, timestamp) = line.strip().split('\t')  
        user_movie.setdefault(userId,{
     })  
        user_movie[userId][itemId] = float(rating)  
          
    movies = {
     }  
    for line in open(filename_movieInfo,'r',encoding='ISO-8859-1'):  
        (movieId, movieTitle) = line.split('|')[0:2]  
        movies[movieId] = movieTitle  
      
    return user_movie, movies  
  
  
def average_rating(user):  
    average = 0  
    for u in user_movie[user].keys():  
        average += user_movie[user][u]  
    average = average * 1.0 / len(user_movie[user].keys())  
    return average  
  
  
def calUserSim(user_movie):  
  
  
    # build inverse table for movie_user  
    movie_user = {
     }  
    for ukey in user_movie.keys():  
        for mkey in user_movie[ukey].keys():  
            if mkey not in movie_user:  
                movie_user[mkey] = []  
            movie_user[mkey].append(ukey)  
  
  
    # calculated co-rated movies between users  
    C = {
     }  
    for movie, users in movie_user.items():  
        for u in users:  
            C.setdefault(u,{
     })  
            for n in users:  
                if u == n:  
                    continue  
                C[u].setdefault(n,[])  
                C[u][n].append(movie)  
                  
  
  
    # calculate user similarity (perason correlation)  
    userSim = {
     }  
    for u in C.keys():  
          
        for n in C[u].keys():  
              
            userSim.setdefault(u,{
     })  
            userSim[u].setdefault(n,0)  
          
            average_u_rate = average_rating(u)  
            average_n_rate = average_rating(n)  
              
            part1 = 0  
            part2 = 0  
            part3 = 0  
            for m in C[u][n]:  
                part1 += (user_movie[u][m]-average_u_rate)*(user_movie[n][m]-average_n_rate)*1.0  
                part2 += pow(user_movie[u][m]-average_u_rate, 2)*1.0  
                part3 += pow(user_movie[n][m]-average_n_rate, 2)*1.0  
                  
            part2 = sqrt(part2)  
            part3 = sqrt(part3)  
            if part2 == 0:  
                part2 = 0.001  
            if part3 == 0:  
                part3 = 0.001   
            userSim[u][n] = part1 / (part2 * part3)     
    return userSim  
  
  
def getRecommendations(user, user_movie, movies, userSim, N):  
    pred = {
     }  
    interacted_items = user_movie[user].keys()  
    average_u_rate = average_rating(user)  
    sumUserSim = 0  
    for n, nuw in sorted(userSim[user].items(),key=itemgetter(1),reverse=True)[0:N]:  
        average_n_rate = average_rating(n)  
        for i, nrating in user_movie[n].items():  
            # filter movies user interacted before  
            if i in interacted_items:  
                continue  
            pred.setdefault(i,0)  
            pred[i] += nuw * (nrating - average_n_rate)  
        sumUserSim += nuw  
  
  
    for i, rating in pred.items():  
        pred[i] = average_u_rate + (pred[i]*1.0) / sumUserSim  
          
    # top-10 pred  
    pred = sorted(pred.items(), key=itemgetter(1), reverse=True)[0:10]  
    return pred    
  
  
if __name__ == "__main__":  
  
  
  
    # load data  
    user_movie, movies = load_data()  
  
  
    # Calculate user similarity  
    userSim = calUserSim(user_movie)  
  
  
    # Recommend  
    pred = getRecommendations('1', user_movie, movies, userSim, 20)  
  
  
    # display recommend result (top-10 results)  
    for i, rating in pred:  
        print ('film: %s,  rating: %s' % (movies[i], rating))
        print ('filmNum: %s,  rating: %s' % (i, rating))

引用作者前者

本文一切用途都是学术用途，没有任何商业行为。