DeepRank: Learning to rank with neural networks for recommendation

摘要

Although, widely applied deep learning models show promising performance in recommender systems, little effort has been devoted to exploring ranking learning in recommender systems. It is important to generate a high quality ranking list for recommender systems, whose ultimate goal is to recommend a ranked list of items for users. Also, the latent features learned from Matrix Factorization (MF) based methods do not take into consideration any deep interactions between the latent features; therefore, they are insufficient to capture user–item latent structures. To address these problems, we propose a novel model, DeepRank, which uses neural networks to improve personalized ranking quality for Collaborative Filtering (CF). This is a general architecture that can not only be easily extended to further research and applications, but also be simplified for pair-wise learning to rank. Finally, we perform extensive experiments on three data sets. Results demonstrate that our proposed models significantly outperform the state-of-the-art approaches. Our projects are available at: https://github.com/XiuzeZhou/deeprank.