Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (2024)

Abstract: 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.

## introduction engaging users in ongoing conversations for personalized recommendations, conversational recommendation systems (crss) @xcite have become a prevalent strategy utilized in diverse fields @xcite . however, crss often face a big challenge known as matthew effect @xcite , captured by the adage "the privileged gain more privilege, while the underprivileged fall further behind." this observation underscores that well-received items or categories in past records garner heightened visibility in future suggestions, whereas less preferred ones frequently face neglect or marginalization. ## experiments and analyses to fully evaluate the proposed hicore, we conduct experiments to answer the following questions: • rq1: how does hicore perform compared with all baselines in the recommendation task? • rq2: how does hicore perform compared with all baselines in the conversation task? • rq3: how does hicore mitigate matthew effect in the crs? • rq4: how do parameters affect our hicore? • rq5: how do different hypergraphs contribute to the performance? ## conclusion the matthew effect poses a significant challenge in the crs due to the dynamic user-system feedback loop, which tends to escalate over time as users engage with the system. in response to these challenges, we proposed a novel framework, hicore, aimed at mitigating the matthew effect by capturing multi-level user interests through a variety of hypergraphs, including item-oriented, entity-oriented, and word-oriented triple-channel hypergraphs. extensive experiments validate that hicore outperforms all baselines, demonstrating the effectiveness of hicore in addressing the matthew effect as users chat with the system over time in the crs.