LightFM is a Python implementation of hybrid recommendation algorithms supporting both implicit and explicit feedback. It provides efficient implementations of BPR and WARP ranking losses for learning-to-rank tasks, and allows incorporating item and user metadata into traditional matrix factorization approaches. The library is designed for fast training via multithreaded model estimation and produces high-quality recommendation results.