Recommend
Simple recommendatnion system implementation with Python
Current model:
- Probabilistic Matrix Factorization
- Bayesian Matrix Factorization
- Alternating Least Squares with Weighted Lambda Regularization (ALS-WR)
Reference:
- "Probabilistic Matrix Factorization", R. Salakhutdinov and A.Mnih., NIPS 2008
- "Bayesian Probabilistic Matrix Factorization using MCMC", R. Salakhutdinov and A.Mnih., ICML 2008
- Matlab code: http://www.cs.toronto.edu/~rsalakhu/BPMF.html
- "Large-scale Parallel Collaborative Filtering for the Netflix Prize", Y. Zhou, D. Wilkinson, R. Schreiber and R. Pan, 2008
Install:
# clone repoisitory
git clone git@github.com:chyikwei/recommend.git
cd recommend
# install numpy & scipy
pip install -r requirements.txt
pip install .
Getting started:
-
A jupyter notbook that compares PMF and BPMF model can be found here.
-
To run BPMF with MovieLens 1M dataset:
First, download MovieLens 1M dataset and unzip it (data will be inml-1mfolder).
Then run:
>>> import numpy as np
>>> from recommend.bpmf import BPMF
>>> from recommend.utils.evaluation import RMSE
>>> from recommend.utils.datasets import load_movielens_1m_ratings
# load user ratings
>>> ratings = load_movielens_1m_ratings('ml-1m/ratings.dat')
>>> n_user = max(ratings[:, 0])
>>> n_item = max(ratings[:, 1])
>>> ratings[:, (0, 1)] -= 1 # shift ids by 1 to let user_id & movie_id start from 0
# fit model
>>> bpmf = BPMF(n_user=n_user, n_item=n_item, n_feature=10,
max_rating=5., min_rating=1., seed=0).fit(ratings, n_iters=20)
>>> RMSE(bpmf.predict(ratings[:, :2]), ratings[:,2]) # training RMSE
0.79784331768263683
# predict ratings for user 0 and item 0 to 9:
>>> bpmf.predict(np.array([[0, i] for i in xrange(10)]))
array([ 4.35574067, 3.60580936, 3.77778456, 3.4479072 , 3.60901065,
4.29750917, 3.66302187, 4.43915423, 3.85788772, 4.02423073])
- Complete examples can be found in
examples/folder. The scripts will download MovieLens 1M dataset automatically, run PMF(BPMF) model and show training/validation RMSE.
Running Test:
python setup.py test
or run test with coverage:
coverage run --source=recommend setup.py test
coverage report -m
Uninstall:
pip uninstall recommend
Notes:
-
Old version code can be found in
v0.0.1. It contains a Probabilistic Matrix Factorization model with theano implementation. -
The previous version (
0.2.1) did not implement correctly MCMC sampling in the BPMF algorithm. In fact, at every timestep it computed the predictions basing on the current value of the feature matrices, and used it to estimate the RMSE. This has no meaning from the MCMC point of view, whose purpose is to sample the feature matrices from the correct distributions in order to estimate the integral through which the rating ditribution is computed.
Instead, the correct approach (see Eq. 10 in reference [2]) entails averaging the predictions at every time step to get a final prediction and compute the RMSE. Essentially, the predicted value itself does not depend only on the last extracted value for the feature matrices, but on the whole chain. Having modified this, the RMSE for both the train and test set with BPMF improves (you can see it in this notebook). (Thanks LoryPack's contribution!)