  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.
