- Add ability to pickle nearest neighbours recommenders (#191)[benfred#191]
- add NDCG method to evaluation (#212)[benfred#212]
- Add a 'recommend_all' method for matrix factorization models (#179[benfred#179]
- Ensure progress bar hits 100% during xval
- Fix bm25recommender missing default parameter on fit
- Fix GPU faiss model with > 1024 results (#149)[benfred#149]
- Add a reddit votes dataseet
- Add similar users calculation in MF modeles (#139)[benfred#139]
- Add an option to whether to include previously liked items or not (#131)[benfred#131]
- Add option for negative preferences to ALS modele (#119)[benfred#119)
- Add filtering negative feedback in test set (#124)[benfred#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