User preferences for products are constantly drifting over time as product perception and popularity are changing when new fashions or products emerge. Therefore, the ability to model the tendency of both user preferences and product attractiveness is vital to the design of recommender systems (RSs). However, conventional methods in RSs are incapable of modeling such a tendency accordingly, leading to unsatisfactory recommendation performance in many real-world deployments. In this paper, we develop a novel probabilistic temporal bilinear model for RSs, exploiting both temporal properties and dynamic information in user preferences and item attractiveness derived from the users' feedback over items, to simultaneously track latent factors that represent user preferences and item attractiveness. A learning and inference algorithm combining a sequential Monte Carlo method and the EM algorithm for this model is also developed to tackle the top-k recommendation problem over time. The proposed model is evaluated on three benchmark datasets. The experimental results demonstrate that our proposed model significantly outperforms a variety of existing methods for top-k recommendation.
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