提出多超图框架，缓解对话推荐中的马太效应
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
- 构建物品、实体、词汇三类超图，捕捉用户多层级兴趣
- 在四个对话推荐数据集上实现新最优性能
- 适合关注公平性与动态反馈的推荐系统研究者
马太效应是推荐系统中的重大挑战：热门项目持续获得关注，冷门项目被忽视，加剧了不公平。尽管已有方法尝试在静态或准静态场景中缓解此问题，但用户长期使用系统时该现象会更严重。为此，本文提出一种新框架HiCore，用于对话推荐系统（CRS）中动态用户-系统反馈循环下的马太效应缓解。通过构建物品、实体、词汇三类多通道超图，学习用户多层次兴趣，有效降低热门项的过度曝光。在四个基于对话推荐的数据集上进行的大量实验表明，HiCore达到了新的最先进性能，验证了其对马太效应的有效缓解能力。代码已开源：https://github.com/zysensmile/HiCore。
原文摘要 · Abstract (English)
The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the static or quasi-static recommendation scenarios, such issue will be more pronounced as users engage with the system over time. To this end, we propose a novel framework, Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (HiCore), aiming to address Matthew effect in the Conversational Recommender System (CRS) involving the dynamic user-system feedback loop. It devotes to learn multi-level user interests by building a set of hypergraphs (i.e., item-, entity-, word-oriented multiple-channel hypergraphs) to alleviate the Matthew effec. Extensive experiments on four CRS-based datasets showcase that HiCore attains a new state-of-the-art performance, underscoring its superiority in mitigating the Matthew effect effectively. Our code is available at https://github.com/zysensmile/HiCore.
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