提出统一去噪框架GUIDER，提升多模态推荐系统准确性
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
- 通过校准策略识别用户交互中的噪声与干净数据
- 在4个公开数据集上显著提升推荐性能，最佳模型增益达12.3%
- 适用于各类多模态推荐系统，可即插即用
多媒体内容的爆发推动了多模态推荐系统（MMRecs）的发展，其利用文本、图像、视频和音频等多种模态实现更个性化的推荐。然而，由于模态间内容错位以及模态语义与推荐语义之间的差距，MMRecs面临严重的数据噪声问题。传统去噪方法难以应对多模态数据的复杂性。为此，我们提出一种通用的、基于引导同步蒸馏的去噪框架GUIDER，旨在通过去噪用户反馈来提升MMRecs性能。具体而言，GUIDER采用重校准策略，从模态内容中识别出干净与噪声交互；引入去噪贝叶斯个性化排序（DBPR）损失函数处理隐式用户反馈；并基于最优传输距离设计去噪知识蒸馏目标，引导模态表示向推荐语义对齐。GUIDER可无缝集成至现有MMRec方法中作为即插即用模块。在四个公开数据集上的实验表明，该方法具备优异的有效性与泛化能力。代码已开源：https://github.com/Neon-Jing/Guider
原文摘要 · Abstract (English)
The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities such as text, images, videos, and audio for more personalized recommendations. However, MMRecs struggle with noisy data caused by misalignment among modal content and the gap between modal semantics and recommendation semantics. Traditional denoising methods are inadequate due to the complexity of multi-modal data. To address this, we propose a universal guided in-sync distillation denoising framework for multi-modal recommendation (GUIDER), designed to improve MMRecs by denoising user feedback. Specifically, GUIDER uses a re-calibration strategy to identify clean and noisy interactions from modal content. It incorporates a Denoising Bayesian Personalized Ranking (DBPR) loss function to handle implicit user feedback. Finally, it applies a denoising knowledge distillation objective based on Optimal Transport distance to guide the alignment from modality representations to recommendation semantics. GUIDER can be seamlessly integrated into existing MMRecs methods as a plug-and-play solution. Experimental results on four public datasets demonstrate its effectiveness and generalizability. Our source code is available at https://github.com/Neon-Jing/Guider
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