Implicit

Breaking API Changes

The API for implicit has substantially changed in v0.5.0 - and any code written for the previous
API will need to be rewritten:

* Change model.fit to take a user_items sparse matrix [484](https://github.com/benfred/implicit/pull/484)
* Return numpy arrays from recommend methods [482](https://github.com/benfred/implicit/pull/482)
* Don't require empty rows in user_items and item_users parameters [526](https://github.com/benfred/implicit/pull/526)
* Unify API for rank_items/recommend/recommend_all [489](https://github.com/benfred/implicit/issues/489)

Performance Improvements

* Speedup evaluation by using batch recommend functions [520](https://github.com/benfred/implicit/pull/520)
* Use FAISS for GPU inference [506](https://github.com/benfred/implicit/pull/506)
* Multithreaded speedups for CPU models [517](https://github.com/benfred/implicit/pull/517)
* Use thrust::binary_search to verify negative samples on GPU [524](https://github.com/benfred/implicit/pull/524)
* Release GIL on GPU calls [528](https://github.com/benfred/implicit/pull/528)

New Features

* Add incremental retraining support for ALS models [527](https://github.com/benfred/implicit/pull/527)
* Add filtering for similar_items and similar_users [488](https://github.com/benfred/implicit/issues/488)
* Add support for recalculate_users/items on the GPU [515](https://github.com/benfred/implicit/pull/515)
* Add methods for converting MF models to/from gpu [521](https://github.com/benfred/implicit/pull/521)
* Add a tutorial notebook for the lastfm example [529](https://github.com/benfred/implicit/pull/529)
* Approximate nearest neighbour for BPR/LMF and GPU models [487](https://github.com/benfred/implicit/issues/487)
* Dynamically detect CUDA availability [174](https://github.com/benfred/implicit/issues/174)

* GPU Inference [406](https://github.com/benfred/implicit/pull/406)
* Fix ALS model for case of > 2^31 interactions [400](https://github.com/benfred/implicit/pull/400)
* Fix GPU dot product when the of factors wasn't warp aligned [427](https://github.com/benfred/implicit/pull/427)
* Use gpu registers for dot product [448](https://github.com/benfred/implicit/pull/448)
* Fix random output with LMF/BPR models [408](https://github.com/benfred/implicit/pull/408)
* Add seed for test-train split [411](https://github.com/benfred/implicit/pull/411)

* Adds Precompiled CUDA packages on conda-forge
* Drops support for CUDA 8

* Implement filter_already_liked_items option [328](https://github.com/benfred/implicit/pull/328)
* Fix bug in ALS explain when user_items contains negative confidence values [313](https://github.com/benfred/implicit/pull/313)
* Improve numerical stability of LMF [383](https://github.com/benfred/implicit/pull/383)
* Add error check after training for NaN factors [381](https://github.com/benfred/implicit/pull/381)
* Support building with Cuda 11

* Add logistic matrix factorization algorithm [231](https://github.com/benfred/implicit/pull/231)
* Use tqdm for progress bars [240](https://github.com/benfred/implicit/pull/240)
* Add AUCK [275](https://github.com/benfred/implicit/pull/275)

* Add ability to pickle nearest neighbours recommenders [191](https://github.com/benfred/implicit/issues/191)
* add NDCG method to evaluation [212](https://github.com/benfred/implicit/pull/212)
* Add a 'recommend_all' method for matrix factorization models [179](https://github.com/benfred/implicit/pull/179)