A python library for implementing a recommender system.
python-recsys is build on top of Divisi2, with csc-pysparse (Divisi2 also requires NumPy, and uses Networkx).
python-recsys also requires SciPy.
To install the dependencies do something like this (Ubuntu):
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
Download python-recsys from github.
tar xvfz python-recsys.tar.gz cd python-recsys sudo python setup.py install
- Load Movielens dataset:
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}) - Compute Singular Value Decomposition (SVD), M=U Sigma V^t:
k = 100 svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, savefile='/tmp/movielens')
- Get similarity between two movies:
ITEMID1 = 1 # Toy Story (1995) ITEMID2 = 2355 # A bug's life (1998) svd.similarity(ITEMID1, ITEMID2) # 0.67706936677315799
- Get movies similar to Toy Story:
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
- Predict the rating a user (USERID) would give to a movie (ITEMID):
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
- Recommend (non-rated) movies to a user:
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
- Which users should see Toy Story? (e.g. which users -that have not rated Toy Story- would give it a high rating?)
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)]
Documentation and examples available here.
To create the HTML documentation files from doc/source do:
cd doc make html
HTML files are created here:
doc/build/html/index.html