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Matrix Factorization Model with Dual Preferences for Rating Prediction

Most matrix factorization methods for rating prediction in recommender system seek to model the preference of each user by a latent feature vector with small dimension. Moreover, these methods assume that user's preferences over all items may be captured by the same vector. However, a given user may have different preferences on a given item when he/she plays different roles in different scenarios. In this paper, we propose a new matrix factorization model named PSVD, which allows us to capture user's different preferences over different items flexibly in rating prediction. Specially, we use a pair of preferences to represent the whole preference of user over items. Then the dual preferences are considered simultaneously in building the latent feature vector of user. Moreover, PSVD model allows users to adjust their own feature vector when selecting different products. A comprehensive set of experiments on three public datasets show that PSVD model can outperform several models in terms of different evaluation metrics.