lightFM is my goto for prototyping Matrix Factorization models. It efficiently handles large data w/ sparse data structures and is CPU accelerated including opt

lightFM is my goto for prototyping Matrix Factorization models. It efficiently handles large data w/ sparse data structures and is CPU accelerated including optimizations like Hogwild!. It also has the WARP loss BPR variant which I have not seen implemented anywhere else. I can train on multi-GB datasets w/ only lightFM and multiple CPUs. Another interesting package is called Implicit. This package, although not as complete as LightFM when it comes to algorithms or APIs, really shines when it comes down to optimizations. Including native Cuda kernels for BPR and ALS it also has an important speedup called the Conjugate Gradient Method which makes it faster than spark in some benchmarks. But usually, now-a-days my work requires more customized hybrid models of which I usually start w/ a base BPR implementation I have in Keras.