Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025)

Abstract: 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.

## introduction conversational recommender systems (crss) have become an important research area @xcite , focusing on understanding user preferences through multi-turn dialogues. however, issues like data imbalance and inconsistent user-item interactions can result in unfair recommendations for disadvantaged users, leading to unsatisfactory suggestions. thus, it is rather crucial to alleviate the unfairness in the dynamic and interactive crs. ## hyfaircrs enhancing fairness in the crs is challenging, as unfairness can worsen over time through dynamic interactions. to this end, we propose hyfaircrs, which combines hypergraph contrastive multi-interest learning with fair crs. the pipeline of hyfaircrs is shown in fig. 1 . ## experiments and analyses to fully evaluate our hyfaircrs, we conduct experiments to answer the following questions: • rq1: how does hyfaircrs perform compared with all baselines in the recommendation task? • rq2: how does hyfaircrs perform compared with all baselines in the conversation task? • rq3: how does hyfaircrs alleviate the unfairness in the crs? • rq4: how does each type of hypergraph contribute to the performance? • rq5: how does hyfaircrs perform on the crossdomain datasets? • rq6: how do parameters affect our hyfaircrs? ## conclusion to enhance recommendation fairness in the crs, we propose hyfaircrs, a novel paradigm that learns diverse interest representations through hypergraphs and line graphs during user interactions via natural language dialogues. these representations are used for generating responses in the conversation task and ensuring fair recommendations in the recommendation task. extensive experiments show that hyfaircrs outperforms strong baselines in reducing unfair recommendations.