Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, next music recommendation and next new music recommendation , to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users' contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users' preferences. To this end, this paper proposes a novel A ttentive T emporal P oint P rocess ( ATPP ) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our ATPP can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, ATPP is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach ATPP outperforms state-of-the-art baselines in both next and next new music recommendation tasks.