Modeling High-order Interactions across Multi-interests for Micro-video Reommendation

• URL: http://arxiv.org/abs/2104.00305v1
• Date: Thu, 1 Apr 2021 07:20:15 GMT
• Title: Modeling High-order Interactions across Multi-interests for Micro-video Reommendation
• Authors: Dong Yao, Shengyu Zhang, Zhou Zhao, Wenyan Fan, Jieming Zhu, Xiuqiang He, Fei Wu
• Abstract summary: 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 model correlation patterns within a specific level.
• Score: 65.16624625748068
• License: http://creativecommons.org/licenses/by/4.0/
• 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 multi-level 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 model correlation patterns within a specific level. Experimental results on filtered public datasets verify that our presented module is useful.

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