technoapurva / theano-bpr

An implementation of Bayesian Personalised Ranking in Theano

theano-bpr

A library implementing Bayesian Personalised Ranking (BPR) for Matrix Factorisation, as described by Rendle et al. in :

http://arxiv.org/abs/1205.2618

This model tries to predict a personalised ranking of items from a user's viewing history. It has been shown to be very efficient for recommendation tasks. It's also used in a variety of other tasks, such as matrix completion, link prediction and tag recommendation.

This library uses Theano and can therefore run on a GPU through CUDA or on the CPU, for which you'll need a working BLAS. We recommend using OpenBlas.

Installation

$ pip install theano-bpr

Usage

An iPython Notebook demonstrating the use of theano-bpr over the Movielens dataset is available in examples/.

Testing

$ nosetests

Licensing terms and authorship

See 'COPYING' and 'AUTHORS' files