Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract)
Keywords:Personalized Recommendation, Attention Mechanism, Multi-level Interest
AbstractPersonalized 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.
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
AAAI Student Abstract and Poster Program