HyCoRec：用于缓解对话推荐中马太效应的超图增强多偏好学习

中文摘要 AI 辅助

马太效应是推荐系统中的一个棘手问题，即热门物品过度曝光而冷门物品常被忽视。多数方法在静态或近静态推荐场景中研究马太效应，但用户与系统长期交互时该效应会加剧。为此提出HyCoRec范式，通过学习多方面偏好（物品、实体、词、评论和知识方面偏好）来缓解对话推荐中的马太效应，可在对话任务中有效生成回复，在推荐任务中准确预测物品。在两个基准上的大量实验验证了HyCoRec取得了新的最优性能及缓解马太效应的优越性。

英文摘要

The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios. However, the Matthew effect will be increasingly amplified when the user interacts with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (HyCoRec), which aims to alleviate the Matthew effect in conversational recommendation. Concretely, HyCoRec devotes to alleviate the Matthew effect by learning multi-aspect preferences, \emph{i.e.}, item-, entity-, word-, review-, and knowledge-aspect preferences, to effectively generate responses in the conversational task and accurately predict items in the recommendation task when the user chats with the system over time. Extensive experiments conducted on two benchmarks validate that HyCoRec achieves new state-of-the-art performance and the superior of alleviating Matthew effect. Our code is available at https://github.com/zysensmile/HyCoRec.

发表机构

• Sun Yat-sen University（中山大学）
• Peng Cheng Laboratory（鹏城实验室）
• South China Agricultural University（华南农业大学）
• Guangdong University of Technology（广东工业大学）
• Jinan University（暨南大学）

机构由 AI 辅助整理，请以论文原文为准。