H y C o R ec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024)

Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), 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, 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.

## introduction conversational recommender systems (crss) engage in iterative conversations with users to provide personalized recommendations @xcite @xcite , which have been widely adopted in various domains such as music recommendation @xcite and online e-commerce @xcite . nevertheless, crss often face the prominent issue of matthew effect @xcite , which can be described as "the rich get richer and the poor get * corresponding author. recently, many research efforts have focused on examining the matthew effect in static or relativelystatic offline recommendation scenarios @xcite @xcite . these offline studies strive to explore the potential causes behind the manifestation of the matthew effect, and two key causes have been identified. one cause @xcite @xcite is that individuals with narrower and less diverse preferences exhibit a higher vulnerability to being trapped within the confines of the matthew effect. another cause @xcite is that the severe popularity bias where popular items consistently receive amplified exposure while less popular ones are underexposed. although these methods have undoubtedly contributed valuable insights into the phenomenon of the matthew effect, they directly overlook the adverse impact stemming from the dynamic user-system feedback loop. more recently, gao et al. @xcite explore the matthew effect in dynamic user-system interactions, but it lacks real-time user engagement through natural language conversations. despite their effectiveness, most methods still suffer from two major limitations. 1) interactive schema. many methods aim to mitigate matthew effect in the static recommendation settings without considering the user-system feedback loop @xcite . in reality, the matthew effect will progressively amplify as users dynamically interact with the system over time. worse still, such amplification will inevitably lead to a series of notorious issues such as filter bubbles @xcite and echo chamber @xcite . thus, it is crucial to consider the dynamic user-system interactions to alleviate matthew effect. 2) preference learning. prior studies @xcite @xcite show that the key to mitigating matthew effect is to learn diverse user preferences. thus, many methods leverage multiplex external knowledge graphs (kgs) to model multi-aspect preferences. but traditional kg edges are limited to linking only two vertices (i.e., factors), restricting preference learning to pairwise interactions. instead, user relations exhibit intricate complexity, such as a user's preference for a garment involves multiple factors like color, brand, style, and texture simultaneously. hence, extending the number of vertices for learning diverse preferences is rather important. to address these issues, we propose a novel paradigm, hypergraph-enhanced multi-preference learning for alleviating matthew effect in conversational recommendation (hycorec), which consists of hypergraph-enhanced multi-preference learning and hypergraph-aware crs. the former aims to model multi-aspect preferences, specifically targeting item-aspect, entity-aspect, word-aspect, review-aspect, and knowledge-aspect preferences. it addresses the matthew effect in crs by utilizing item-based hypergraph, entitybased hypergraph, word-based hypergraph, item reviews, and knowledge graphs to learn and derive these preferences. the latter focuses on leveraging these multi-aspect preferences as users interact with the system. concretely, multi-aspect preferences are adopting to accurately predict the next utterances in the conversational task, and effectively make diverse item predictions in the recommendation task. by incorporating and utilizing these multi-aspect preferences, the system aims to provide precise and diverse recommendations that cater to the individual user's preferences and needs for alleviating the matthew effect as they continue to engage with the system. empirically, extensive experimental results on two benchmarks show that hycorec outperforms all the compared baselines, and the superior of mitigating matthew effect. overall, our main contributions are included: • to the best of our knowledge, this is the first work to model multi-aspect user preferences, i.e., item-, entity-, word-, review-, knowledge-aspect preference, to alleviate matthew effect in the crs. ## hycorec matthew effect is a notorious issue in the crs, and it inevitably becomes intensified over time due to the existence of the dynamic user-system feedback loop. to tackle these challenges, we propose a novel paradigm, hycorec, which consists of hypergraph-enhanced multi-preference learning and hypergraph-aware crs. the overall pipeline of our hycorec is depicted in fig. 1 . ## experiments and analyses we conduct experiments to fully evaluate our hy-corec and answer the following questions: • rq1: how does hycorec perform compared with all baselines in the recommendation task? • rq2: how does hycorec perform compared with all baselines in the conversation task? model redial tg-redial r@10 r@50 m@10 m@50 n@10 n@50 r@10 r@50 m@10 m@50 n@10 n@50 • rq3: how does hycorec alleviate matthew effect in the crs? ## conclusion the matthew effect is a notorious issue in the crs, and it will be increasingly amplified due to the dynamic user-system feedback loop. to address these issues, we propose a novel paradigm, hycorec, which aims to learn multi-aspect user preferences, i.e., item-, entity-, word-, review-, and knowledgeaspect preferences, to effectively generate diverse responses in the conversation task and accurately predict items in the recommendation task for alleviating matthew effect. extensive experiments validate that our hycorec outperforms all the compared baselines and the superior of hycorec in alleviating matthew effect in the crs.