Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract)

Authors

  • Dong Yao Zhejiang University
  • Shengyu Zhang Zhejiang University
  • Zhou Zhao Zhejiang University
  • Wenyan Fan Zhejiang University
  • Jieming Zhu Huawei
  • Xiuqiang He Huawei
  • Fei Wu Zhejiang University

Keywords:

Personalized Recommendation, Attention Mechanism, Multi-level Interest

Abstract

Personalized recommendation system has become pervasive in various video platform.Many effective methods have been proposed, but most of them didn’t capture the user’s multilevel interest trait and dependencies between their viewed micro-videos well. To solve these problems, we propose a Self-over-Co Attention module to enhance user’s interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self attention to modelcorrelation patterns within a specific level. Experimental results on filtered public datasets verify that our presented module is useful.

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Published

2021-05-18

How to Cite

Yao, D., Zhang, S., Zhao, Z., Fan, W., Zhu, J., He, X., & Wu, F. (2021). Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15945-15946. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17969

Issue

Section

AAAI Student Abstract and Poster Program