Abstract
Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose: (1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework - TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.