DeepRS: A Library of Recommendation Algorithms Based on Deep Learning

Abstract In recent years, recommendation systems have become more complex with increasing research on user preferences. Recommendation algorithm based deep learning has attracted a lot of attention from researchers in academia and industry, many new models are proposed every year. Researchers often need to implement the model compare results, which is great challenge. Even if some papers provide source code, there variety programming languages or frameworks, it not easy results different frameworks. view lack easily extensible learning-based libraries, common analysis algorithms factorization machine (AFM), neural (NFM), (DeepFM) cross-network (DCN), library (DeepRS for short) designed implemented. It consists three levels: framework level, abstract level level. The adopts Tensorflow open framework, provides interfaces, such as automatic differentiation, tensor computing, GPU numerical optimization algorithms. abstraction uses interface realize embedding layer (EL), full connection (FCL), multi-layer perceptron (MLPL), prediction (PL), (FML), network (ANL), cross-layer (CL) (CNL). implements algorithms, AFM, NFM, DeepFM DCN, basis Experiments show that good scalability, ease use correctness.