Fast Python Collaborative Filtering for Implicit Datasets.
This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets:
- Alternating Least Squares as described in the papers Collaborative Filtering for Implicit Feedback Datasets and in Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.
- Bayesian Personalized Ranking
- Logistic Matrix Factorization
- Item-Item Nearest Neighbour models, using Cosine, TFIDF or BM25 as a distance metric
All models have multi-threaded training routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores. In addition, the ALS and BPR models both have custom CUDA kernels - enabling fitting on compatible GPU's. This library also supports using approximate nearest neighbours libraries such as Annoy, NMSLIB and Faiss for speeding up making recommendations.
.. toctree::
:maxdepth: 2
:caption: Contents:
Quickstart <quickstart>
RecommenderBase <models>
Alternating Least Squares <als>
Bayesian Personalized Ranking <bpr>
Logistic Matrix Factorization <lmf>
Approximate Alternating Least Squares <ann>