发表机构
Nanyang Technological University; Digital Trust Centre Singapore; Sun Yat-sen University; South China Agricultural University; Peng Cheng Laboratory（南洋理工大学; 新加坡数字信任中心; 中山大学; 华南农业大学; 鹏城实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对对话推荐系统中的马太效应问题，提出多超图增强多兴趣自监督学习框架HiCore，通过构建超图学习用户多层次兴趣，在四个相关数据集实验中取得新最优性能，有效缓解了马太效应。
AI 中文摘要
马太效应是推荐系统中的重大挑战，热门物品受关注增多，冷门物品常被忽视，加剧差距。虽有方法缓解静态或准静态推荐场景下的马太效应，但在动态用户-系统反馈循环的对话推荐系统中更明显。为此提出多超图增强多兴趣自监督学习框架（HiCore），通过构建超图学习多层次用户兴趣来缓解马太效应。在四个基于对话推荐系统的数据集上的实验表明，HiCore取得新的最优性能，有效缓解了马太效应。
英文摘要
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.
