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This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets. 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 neighbour libraries such as Annoy, NMSLIB and Faiss for speeding up making recommendations.
Features
- Fast Python Collaborative Filtering for Implicit Datasets
- Logistic Matrix Factorization
- Bayesian Personalized Ranking
- Documentation available
- Implicit can be installed from pypi
- Examples included
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow Implicit
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