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

2026-07-20

AI Summary

This work addresses the exacerbated Matthew effect in conversational recommender systems caused by dynamic user-item interactions. To mitigate popularity bias and enhance long-tail item coverage, the authors propose a novel multi-channel hypergraph architecture that jointly models items, knowledge graph entities, and words to construct a multi-granular representation of user interests. By integrating a multi-interest self-supervised learning mechanism, the approach effectively balances recommendation accuracy with fairness. Extensive experiments on four standard conversational recommendation benchmarks demonstrate that the proposed method consistently achieves state-of-the-art performance, significantly improving both recommendation fairness and effectiveness for long-tail items.

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.

Problem

• Matthew effect
• Conversational Recommender System
• popularity bias
• dynamic feedback loop
• recommendation disparity

Innovation

• Matthew effect
• conversational recommendation
• multi-interest learning
• hypergraph
• self-supervised learning