2021/04/01 by Dong Yao, Shengyu Zhang, Yao, Dong +11
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #cs.CV #cs.IR
paper · pdf · doi:10.48550/arxiv.2104.00305
accepted to AAAI 2021
openalex publication_date 2021/04/01 · arxiv created 2021/05/10 · arxiv updated 2021/05/11 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
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.