推荐系统之基于物品的协同过滤算法(ItemCF)

发布时间：2018-03-04 16:55,

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ItemCF

推荐系统之基于物品的协同过滤算法(ItemCF)

前端时间已经把基于用户的推荐系统给弄出来了，详情见我的另一篇文章：点击打开链接

，(建议先看懂UserCF后再来看这篇文章，当然大佬可以忽视)其实理解了基于用户的协同过滤算法，再来看基于物品的协同过滤算法，就会感觉没啥太大差异，

具体的思路，通俗的讲：用户A 喜欢了一个物品s集合,那么推荐的时候就把与物品s集合里最相似的前N个物品推荐给用户A，结束。

是不是言简意赅？哈哈，其实道理都差不多，看懂了UserCF再来看ItemCF,就会感觉基本差不多。具体的步骤呢：

一、计算物品之间的相似度。

二、根据物品的相似度和用户的历史行为给用户生成推荐列表

同样，计算相似度的时候公式用的也是余弦相似度，详情就看我写的UserCF吧：点击打开链接

，因为都差不多就不重复写了，对照着上一篇博客然后在看看书，就知道基本完全一样了

这里就贴上书上给的一个案例，最下面就是系统的推荐TOP N ，很好理解：

依旧是大牛的ItemCF 代码，贴上供大家学习：#-*- coding: utf-8 -*- ''' Created on 2015-06-22

@author: Lockvictor ''' import sys import random import math import os from

operator import itemgetter random.seed(0) class ItemBasedCF(object): ''' TopN

recommendation - Item Based Collaborative Filtering ''' def __init__(self):

self.trainset = {} self.testset = {} self.n_sim_movie = 20 self.n_rec_movie =

10 self.movie_sim_mat = {} self.movie_popular = {} self.movie_count = 0

print('Similar movie number = %d' % self.n_sim_movie, file=sys.stderr)

print('Recommended movie number = %d' % self.n_rec_movie, file=sys.stderr)

@staticmethod def loadfile(filename): ''' load a file, return a generator. '''

fp = open(filename, 'r') for i, line in enumerate(fp): yield line.strip('\r\n')

if i % 100000 == 0: print ('loading %s(%s)' % (filename, i), file=sys.stderr)

fp.close() print ('load %s succ' % filename, file=sys.stderr) def

generate_dataset(self, filename, pivot=0.7): ''' load rating data and split it

to training set and test set ''' trainset_len = 0 testset_len = 0 for line in

self.loadfile(filename): user, movie, rating, _ = line.split('::') # split the

data by pivot if random.random() < pivot: self.trainset.setdefault(user, {})

self.trainset[user][movie] = int(rating) trainset_len += 1 else:

self.testset.setdefault(user, {}) self.testset[user][movie] = int(rating)

testset_len += 1 print ('split training set and test set succ',

file=sys.stderr) print ('train set = %s' % trainset_len, file=sys.stderr) print

('test set = %s' % testset_len, file=sys.stderr) def calc_movie_sim(self): '''

calculate movie similarity matrix ''' print('counting movies number and

popularity...', file=sys.stderr) for user, movies in self.trainset.items(): for

movie in movies: # count item popularity if movie not in self.movie_popular:

self.movie_popular[movie] = 0 self.movie_popular[movie] += 1 print('count

movies number and popularity succ', file=sys.stderr) # save the total number of

movies self.movie_count = len(self.movie_popular) print('total movie number =

%d' % self.movie_count, file=sys.stderr) # count co-rated users between items

itemsim_mat = self.movie_sim_mat print('building co-rated users matrix...',

file=sys.stderr) for user, movies in self.trainset.items(): for m1 in movies:

for m2 in movies: if m1 == m2: continue itemsim_mat.setdefault(m1, {})

itemsim_mat[m1].setdefault(m2, 0) itemsim_mat[m1][m2] += 1 print('build

co-rated users matrix succ', file=sys.stderr) # calculate similarity matrix

print('calculating movie similarity matrix...', file=sys.stderr)

simfactor_count = 0 PRINT_STEP = 2000000 for m1, related_movies in

itemsim_mat.items(): for m2, count in related_movies.items():

itemsim_mat[m1][m2] = count / math.sqrt( self.movie_popular[m1] *

self.movie_popular[m2]) simfactor_count += 1 if simfactor_count % PRINT_STEP ==

0: print('calculating movie similarity factor(%d)' % simfactor_count,

file=sys.stderr) print('calculate movie similarity matrix(similarity factor)

succ', file=sys.stderr) print('Total similarity factor number = %d' %

simfactor_count, file=sys.stderr) def recommend(self, user): ''' Find K similar

movies and recommend N movies. ''' K = self.n_sim_movie N = self.n_rec_movie

rank = {} watched_movies = self.trainset[user] for movie, rating in

watched_movies.items(): for related_movie, similarity_factor in

sorted(self.movie_sim_mat[movie].items(), key=itemgetter(1), reverse=True)[:K]:

if related_movie in watched_movies: continue rank.setdefault(related_movie, 0)

rank[related_movie] += similarity_factor * rating # return the N best movies

return sorted(rank.items(), key=itemgetter(1), reverse=True)[:N] def

evaluate(self): ''' print evaluation result: precision, recall, coverage and

popularity ''' print('Evaluation start...', file=sys.stderr) N =

self.n_rec_movie # varables for precision and recall hit = 0 rec_count = 0

test_count = 0 # varables for coverage all_rec_movies = set() # varables for

popularity popular_sum = 0 for i, user in enumerate(self.trainset): if i % 500

== 0: print ('recommended for %d users' % i, file=sys.stderr) test_movies =

self.testset.get(user, {}) rec_movies = self.recommend(user) for movie, _ in

rec_movies: if movie in test_movies: hit += 1 all_rec_movies.add(movie)

popular_sum += math.log(1 + self.movie_popular[movie]) rec_count += N

test_count += len(test_movies) precision = hit / (1.0 * rec_count) recall = hit

/ (1.0 * test_count) coverage = len(all_rec_movies) / (1.0 * self.movie_count)

popularity = popular_sum / (1.0 * rec_count) print

('precision=%.4f\trecall=%.4f\tcoverage=%.4f\tpopularity=%.4f' % (precision,

recall, coverage, popularity), file=sys.stderr) if __name__ == '__main__':

ratingfile = os.path.join('ml-1m', 'ratings.dat') itemcf = ItemBasedCF()

itemcf.generate_dataset(ratingfile) itemcf.calc_movie_sim() itemcf.evaluate()

代码来源：https://github.com/Lockvictor/MovieLens-RecSys