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

TL;DR

This paper introduces a novel self-over-co attention module that models multi-level user interests and dependencies in micro-video recommendation systems, improving interest representation accuracy.

Contribution

The paper proposes a new self-over-co attention module to better capture multi-level interests and dependencies in user behavior for micro-video recommendation.

Findings

Experimental results verify the effectiveness of the proposed module.

The module improves interest representation in recommendation tasks.

The approach outperforms baseline methods on public datasets.

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.