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Enriching User Representation in Neural Matrix Factorization

Personalized recommendation is a crucial problem in the era of information overload. One of the most prominent methods addressing this problem is collaborative filtering (CF). Recently, NeuMF [1] has employed Deep Neural Network to facilitate traditional CF methods. Although NeuMF achieved the state-of-the-art performance, it is expected that predictive accuracy can be improved by enriching user representation. In this paper, we propose a neural network incorporating latent features and personalized information of users, namely Feature based Neural Matrix Factorization (FeaNMF). In FeaNMF, personalized information can be considered as auxiliary features which are generated by associating past behavior of users (e.g, purchase history, browsing activity, watching habits) with item categories. By explicitly modeling personalized information in the metric of item categories, the major advantages of FeaNMF are: (1) it enriches user representation and thus enhances the predictive capability, (2) it inherits the advantages of NeuMF to model the latent features in a more comprehensive approach, rather than applying a simple linear function as existing methods (e.g. Matrix Factorization), and (3) it is easy to extend FeaNMF for employing a large amount of external sources to construct user preferences. Experiments show that our model significantly improves predictive accuracy compared to the three previous approaches including NeuMF.