Abstract:
Because user preferences change over time, capturing them is essential to predicting user behavior. Many shallow and deep recommendation algorithms model user static and dynamic preferences separately, making it difficult to fuse them for recommendation. This paper addresses the problem of translating a user’s sequential behavior into their latent preferences. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user’s dynamic preferences conditioned on his/her past b Qualitative studies on the Netflix dataset show that the proposed method can capture user preference drifts over time, and quantitative studies on multiple real-world datasets show that it outperforms state-of-the-art factorization and neural sequential recommendation methods.
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