Proceedings

Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

Gaode Chen, Xinghua Zhang, Yanyan Zhao, Cong Xue, Ji Xiang

Download BibTeX Cite

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1426-1433.

https://doi.org/10.24963/ijcai.2021/197

Keywords

  • Machine Learning: Recommender Systems
  • Data Mining: Big Data, Large-Scale Systems
  • Data Mining: Recommender Systems

Abstract

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

Gaode Chen, Xinghua Zhang, Yanyan Zhao, Cong Xue, and Ji Xiang. 2021. Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 1426–1433. https://doi.org/10.24963/ijcai.2021/197
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards