python-recsys：一款实现推荐系统的python库

python-recsys是一个用来实现推荐系统的python库。

安装

依赖项

python-recsys构建于Divisi2（基于语义网络的常识推理库）之上，使用了csc-pysparse（稀疏矩阵计算库），而Divisi2依赖于NumPy和Networkx库。另外python-recsys也依赖于SciPy库。

安装依赖库过程如下（以Ubuntu为例）：

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sudo apt-get install python-scipy python-numpy sudo apt-get install python-pip sudo pip install csc-pysparse networkx divisi2   # If you don't have pip installed then do: # sudo easy_install csc-pysparse # sudo easy_install networkx # sudo easy_install divisi2

先从github上下载安装文件，再安装python-recsys：

Shell

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tar xvfz python-recsys.tar.gz cd python-recsys sudo python setup.py install

示例

1. 加载Movielens数据集:

Python

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from recsys.algorithm.factorize import SVD svd = SVD() svd.load_data(filename='./data/movielens/ratings.dat',             sep='::',             format={'col':0, 'row':1, 'value':2, 'ids': int})

2. 进行奇异值分解 (SVD), M=U Sigma V^t:

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k = 100 svd.compute(k=k,             min_values=10,             pre_normalize=None,             mean_center=True,             post_normalize=True,             savefile='/tmp/movielens')

3. 得到两部电影的相似性:

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ITEMID1 = 1    # Toy Story (1995) ITEMID2 = 2355 # A bug's life (1998)   svd.similarity(ITEMID1, ITEMID2) # 0.67706936677315799

4. 获得和电影Toy Story相似的电影:

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svd.similar(ITEMID1)   # Returns: <ITEMID, Cosine Similarity Value> [(1,    0.99999999999999978), # Toy Story (3114, 0.87060391051018071), # Toy Story 2 (2355, 0.67706936677315799), # A bug's life (588,  0.5807351496754426),  # Aladdin (595,  0.46031829709743477), # Beauty and the Beast (1907, 0.44589398718134365), # Mulan (364,  0.42908159895574161), # The Lion King (2081, 0.42566581277820803), # The Little Mermaid (3396, 0.42474056361935913), # The Muppet Movie (2761, 0.40439361857585354)] # The Iron Giant

5. 预测一个用户 (USERID) 将给一部电影 (ITEMID)的打分:

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MIN_RATING = 0.0 MAX_RATING = 5.0 ITEMID = 1 USERID = 1   svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING) # Predicted value 5.0   svd.get_matrix().value(ITEMID, USERID) # Real value 5.0

6. 推荐 (没被用户打过分的) 电影给用户:

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svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False   # Returns: <ITEMID, Predicted Rating> [(2905, 5.2133848204673416), # Shaggy D.A., The (318,  5.2052108435956033), # Shawshank Redemption, The (2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven) (1178, 5.0962756861447023), # Paths of Glory (1957) (904,  5.0771405690055724), # Rear Window (1954) (1250, 5.0744156653222436), # Bridge on the River Kwai, The (858,  5.0650911066862907), # Godfather, The (922,  5.0605327279819408), # Sunset Blvd. (1198, 5.0554543765500419), # Raiders of the Lost Ark (1148, 5.0548789542105332)] # Wrong Trousers, The

7. 哪些用户应该会看Toy Story (哪些没给Toy Story打过分的用户将给它一个高的打分?)？

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svd.recommend(ITEMID)   # Returns: <USERID, Predicted Rating> [(283,  5.716264440514446), (3604, 5.6471765418323141), (5056, 5.6218800339214496), (446,  5.5707524860615738), (3902, 5.5494529168484652), (4634, 5.51643364021289), (3324, 5.5138903299082802), (4801, 5.4947999354188548), (1131, 5.4941438045650068), (2339, 5.4916048051511659)]

文档

从doc/source目录创建HTML文档：

1 2	cd doc make html

HTML 将被创建在下面路径中:

1	doc/build/html/index.html

开源地址：https://github.com/ocelma/python-recsys