A python library for implementing a recommender system
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README.rst

python-recsys

A python library for implementing a recommender system.

Installation

Dependencies

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

Download python-recsys from github.

Install

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

Example

  1. 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})
  1. 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')
  1. Get similarity between two movies:
ITEMID1 = 1    # Toy Story (1995)
ITEMID2 = 2355 # A bug's life (1998)

svd.similarity(ITEMID1, ITEMID2)
# 0.67706936677315799
  1. 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
  1. 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
  1. 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
  1. 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

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