- Adds Precompiled CUDA packages on conda-forge
- Drops support for CUDA 8
- Implement filter_already_liked_items option #328
- Fix bug in ALS explain when user_items contains negative confidence values #313
- Improve numerical stability of LMF #383
- Add error check after training for NaN factors #381
- Support building with Cuda 11
- Add ability to pickle nearest neighbours recommenders #191
- add NDCG method to evaluation #212
- Add a 'recommend_all' method for matrix factorization models #179
- Ensure progress bar hits 100% during xval
- Fix bm25recommender missing default parameter on fit
- Fix GPU faiss model with > 1024 results #149
- Add a reddit votes dataseet
- Add similar users calculation in MF modeles #139
- Add an option to whether to include previously liked items or not #131
- Add option for negative preferences to ALS modele #119
- Add filtering negative feedback in test set #124
- 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