We attempt to address a case of the Recommendation for Repeat Consumption (RRC) problem by proposing a Time-Sensitive Personalized Pairwise Ranking (TS-PPR) model based on the behavioral features extracted from user implicit feedback in the consumption history. TS-PPR factorizes the temporal useritem interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.
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