AAAI 2026technical0 citations
When Top-ranked Recommendations Fail: Modeling Multi-Granular Negative Feedback for Explainable and Robust Video Recommendation
Siran Chen, Boyu Chen, Chenyun Yu, Yi Ouyang, Lei Cheng, Chengxiang Zhuo, Zang Li, Yali Wang
Abstract
Existing video recommendation systems, relying mainly on ID-based embedding mapping and collaborative filtering, often fail to capture in-depth video content semantics. Moreover, most struggle to address biased user behaviors (e.g., accidental clicks, fast skips), leading to inaccurate interest modeling and frequent negative feedback in top recommendations with unclear causes. To tackle this issue, we collect real-world user video-watching sequences, annotate the reasons for users
BibTeX
@inproceedings{aaai2026_whentoprankedrec,
title = {When Top-ranked Recommendations Fail: Modeling Multi-Granular Negative Feedback for Explainable and Robust Video Recommendation},
author = {Siran Chen and Boyu Chen and Chenyun Yu and Yi Ouyang and Lei Cheng and Chengxiang Zhuo and Zang Li and Yali Wang},
booktitle = {AAAI 2026},
year = {2026}
}