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

Autor:	Yao, Dong, Zhang, Shengyu, Zhao, Zhou, Fan, Wenyan, Zhu, Jieming, He, Xiuqiang, Wu, Fei
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted to AAAI 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2104.00305 Zobrazit plný text záznamu View this record from Arxiv