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Item_Based_CF_Recommendation_Engine.py
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import math
import random
import pandas as pd
from operator import itemgetter
# 基于物品的协同过滤推荐
class ItemCF():
# 初始化参数
def __init__(self):
# 推荐10部电影
self.n_sim_movies = 10
self.n_rec_movies = 10
# 划分训练集与测试集
self.ratio = 0.7
self.train = {}
self.test = {}
# 电影元数据
self.movies = 0
self.m_cnt = 0
# 电影相似度度矩阵
self.m_sim_matrix = {}
print('Recommending {0} movies'.format(self.n_rec_movies))
# 载入数据
def load_file(self, filepath):
with open(filepath, 'r') as f:
for i, line in enumerate(f):
if i == 0:
continue
yield line.strip('\r\n')
# 划分训练集与测试集
def split_dataset(self, filepath):
print('Spliting dataset...')
train_len = 0
test_len = 0
for line in self.load_file(filepath):
u_id, m_id, rating, timestamp = line.split(',')
if random.random() < self.ratio:
self.train.setdefault(u_id, {})
self.train[u_id][m_id] = rating
train_len += 1
else:
self.test.setdefault(u_id, {})
self.test[u_id][m_id] = rating
test_len += 1
print('Done!')
print('Train: {0}, Test: {1}'.format(train_len, test_len))
# 处理电影名
def reset_title(self, title):
if ',' in title:
return ' '.join(title[:-7].split(',')[::-1]).strip()
else:
return title[:-7]
# 清洗数据，获得电影元数据
def movie_metadata(self, m_csv):
print('Building movie metadata...')
self.movies = pd.read_csv(m_csv)
self.m_cnt = len(self.movies)
self.movies['year'] = self.movies['title'].apply(lambda x: x[-5:-1])
self.movies['title'] = self.movies['title'].apply(self.reset_title)
print('Done!')
# 计算电影相似度
def cal_m_sim(self):
print('Calculating movie similarity matrix...')
# 计算相似度矩阵
m_popularity = {}
for user, movies in self.train.items():
for m1 in movies:
m_popularity.setdefault(m1, 0)
m_popularity[m1] += 1
for m2 in movies:
if m1 == m2:
continue
self.m_sim_matrix.setdefault(m1, {})
self.m_sim_matrix[m1].setdefault(m2, 0)
self.m_sim_matrix[m1][m2] += 1
for m1, m1_related in self.m_sim_matrix.items():
for m2, count in m1_related.items():
self.m_sim_matrix[m1][m2] = count / math.sqrt(m_popularity[m1] * m_popularity[m2])
print('Done!')
# 匹配电影信息
def get_info(self, rank):
rank = sorted(rank.items(), key=itemgetter(1), reverse=True)[:self.n_sim_movies]
rank_df = pd.DataFrame(rank, columns=['movieId', 'similarity'])
rank_df['movieId'] = rank_df['movieId'].apply(lambda x: int(x))
m_rec = pd.merge(rank_df, self.movies, how='left', on='movieId')
m_rec = m_rec[['movieId', 'title', 'year']]
return m_rec
# 推荐电影
def recommend(self, u_id):
watched = self.train[u_id]
rank = {}
for m_id, rating in watched.items():
n_sim_movies = sorted(self.m_sim_matrix[m_id].items(), key=itemgetter(1), reverse=True)[:self.n_sim_movies]
for m, similarity in n_sim_movies:
if m not in watched:
rank.setdefault(m, 0)
rank[m] += similarity * float(rating)
m_rec = self.get_info(rank)
return m_rec
# 推荐评估
def evaluate(self):
print('Evaluation start...')
hit = 0
rec_cnt = 0
test_cnt = 0
m_all_rec = set()
for u_id in self.train:
m_test = self.test.get(u_id, {})
m_rec = self.recommend(u_id)
m_rec = list(m_rec['movieId'])
for m_id in m_rec:
if str(m_id) in m_test:
hit += 1
m_all_rec.add(m_id)
rec_cnt += self.n_rec_movies
test_cnt += len(m_test)
precision = hit / rec_cnt
recall = hit / test_cnt
coverage = len(m_all_rec) / self.m_cnt
result = (precision, recall, coverage)
print('Precision = %.4f\nRecall = %.4f\nCoverage = %.4f' % result)
if __name__ == '__main__':
m_csv = '/Users/vita/Movie_Recommender/input_data/small/movies.csv'
r_csv = '/Users/vita/Movie_Recommender/input_data/small/ratings.csv'
u_id = '3'
engine = ItemCF()
engine.split_dataset(r_csv)
engine.movie_metadata(m_csv)
engine.cal_m_sim()
print('For the userId = {0}, 10 movies are recommended as follows:'.format(u_id))
print(engine.recommend(u_id))
engine.evaluate()
###########################################################################
# Recommending 10 movies... #
# Splitng train and test dataset... #
# Done! #
# Train: 70616, Test: 30220 #
# Preparing movie metadata... #
# Done! #
# Calculating movie similarity matrix... #
# Calculation done! #
# for userId=3, 10 movies are recommended as follows: #
# movieId title year #
# 870 Gone Fishin' 1997 #
# 1839 My Giant 1998 #
# 2368 King Kong Lives 1986 #
# 3527 Predator 1987 #
# 2661 It Came from Outer Space 1953 #
# 4496 D.O.A. 1988 #
# 6371 Pokémon Heroes 2003 #
# 7040 To Live and Die in L.A. 1985 #
# 3648 The Abominable Snowman 1957 #
# 4942 The Angry Red Planet 1959 #
# Evaluation start... #
# Precision = 0.2846 #
# Recall = 0.0575 #
# Coverage = 0.0818 #
###########################################################################