- Adds evaluation functionality with functions for computing P@k and MAP@K and generating a train/test split
- BPR model now verifies negative samples haven’t been actually liked now, leading to more accurate recommendations
- Faster KNN recommendations (up to 10x faster recommend calls)
- Various fixes for models when fitting on the GPU
- Fix CUDA install on Windows
- Display progress bars when fitting models using tqdm
- More datasets: added million song dataset, sketchfab, movielens 100k, 1m and 10m
- Use HDF5 files for distributing datasets
- Add rank_items method to recommender
- Fix issue with last user having no ratings in BPR model
- Support more than 2^31 training examples in ALS and BPR models
- Allow 64 bit factors for BPR
- Add a Bayesian Personalized Ranking model, with an option for fitting on the GPU
- Add Support for ANN libraries likes Faiss, NMSLIB and Annoy for making recommendations