Implicit

* Ensure progress bar hits 100% during xval
* Fix bm25recommender missing default parameter on fit

* Fix GPU faiss model with > 1024 results [149](https://github.com/benfred/implicit/issues/149)
* Add a reddit votes dataseet
* Add similar users calculation in MF modeles [139](https://github.com/benfred/implicit/pull/139)
* Add an option to whether to include previously liked items or not [131](https://github.com/benfred/implicit/issues/13)
* Add option for negative preferences to ALS modele [119](https://github.com/benfred/implicit/issues/119)
* Add filtering negative feedback in test set [124](https://github.com/benfred/implicit/issues/124)

* Adds evaluation functionality with functions for computing Pk and MAPK 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