The library implements several recommendation algorithms including Alternating Least Squares, Bayesian Personalized Ranking, and Logistic Matrix Factorization. It uses Cython and OpenMP for multi-threaded CPU training and includes custom CUDA kernels for GPU acceleration. It also integrates with approximate nearest neighbor libraries like Annoy, NMSLIB, and Faiss for efficient recommendation retrieval.