通过超图对比学习，提升对话推荐系统中多兴趣公平性。
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
- 用对比学习构建多样超图捕捉用户多兴趣
- 在动态交互中显著降低推荐不公平性
- 适合关注对话推荐公平性的研究者与工程师
推荐系统中的不公平问题长期存在，常导致基于性别、种族、年龄或流行度等属性的偏差。尽管已有方法在离线或静态场景下改善了公平性，但随着时间推移，不公平性可能加剧，引发马太效应、信息茧房和回音室等问题。为此，本文提出一种新框架 HyFairCRS（Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System），旨在提升动态交互式对话推荐系统中的多兴趣多样性公平性。HyFairCRS 首先通过对比学习构建多样化超图，全面捕捉用户多兴趣；随后在对话过程中利用这些兴趣生成信息丰富响应，并确保在动态用户-系统反馈循环中实现公平的物品预测。在两个基于对话推荐系统的数据集上的实验表明，HyFairCRS 达到新的最先进性能，同时有效缓解了不公平现象。代码已公开于 https://github.com/zysensmile/HyFairCRS。
原文摘要 · Abstract (English)
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyFairCRS), aiming to promote multi-interest diversity fairness in dynamic and interactive Conversational Recommender Systems (CRSs). HyFairCRS first captures a wide range of user interests by establishing diverse hypergraphs through contrastive learning. These interests are then utilized in conversations to generate informative responses and ensure fair item predictions within the dynamic user-system feedback loop. Experiments on two CRS-based datasets show that HyFairCRS achieves a new state-of-the-art performance while effectively alleviating unfairness. Our code is available at https://github.com/zysensmile/HyFairCRS.
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