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Deep Matrix Factorization Models for Recommender Systems

Deep Matrix Factorization Models for Recommender Systems Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, Jiajun ChenNational Key Laboratory for Novel Software Technology; Nanjing University, Nanjing 210023, ChinaCollaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, Systems usually make personalizedrecommendation with user-item interaction ratings,implicit feedback and auxiliary information. Ma-trix Factorization is the basic idea to predict a per-sonalized ranking over a set of items for an indi-vidual user with the similarities among users anditems.

andF denotes the function that maps the model parameters to the predicted scores. Based on this function, we can achieve our goal of recommending a set of items for an individual user to maximize the user’s satisfaction. Now, the next question is how to define such a functionF . Latent Factor Model (LFM) simply applied the dot product of p i, q

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