A python implementation of the generic factorization machines model class adapted for collaborative filtering recommendation problems with implicit feedback user-item interaction data and (optionally) additional user/item side features
RankFM is a python implementation of the general Factorization Machines model class adapted for collaborative filtering recommendation/ranking problems with implicit feedback user/item interaction data. It uses Bayesian Personalized Ranking (BPR) and a variant of Weighted Approximate-Rank Pairwise (WARP) loss to learn model weights via Stochastic Gradient Descent (SGD). It can (optionally) incorporate sample weights and user/item auxiliary features to augment the main interaction data. The core (training, prediction, recommendation) methods are written in Cython, making it possible to scale to millions of user/item interactions. Designed for ease-of-use, RankFM accepts both pd.DataFrame and np.ndarray inputs - you do not have to convert your data to scipy.sparse matrices or re-map user/item identifiers prior to use. RankFM internally maps all user/item identifiers to zero-based integer indexes, but always converts its output back to the original user/item identifiers from your data, which can be arbitrary (non-zero-based, non-consecutive) integers or even strings.
