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

arXiv: https://arxiv.org/abs/2104.00305 · HTML: https://mlsanity.com/paper/2104.00305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation](https://mlsanity.com/paper/1711.06632/) (2017)
- [Comprehensive Information Integration Modeling Framework for Video Titling](https://mlsanity.com/paper/2006.13608/) (2020)
- [Poet: Product-oriented Video Captioner for E-commerce](https://mlsanity.com/paper/2008.06880/) (2020)
- [Future-Aware Diverse Trends Framework for Recommendation](https://mlsanity.com/paper/2011.00422/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation](https://mlsanity.com/paper/2501.02666/) (2025)

## Abstract (arXiv)

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.