Source-linked AI summary
Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam
TL;DR
The paper addresses the Matthew effect in conversational recommendation, where dynamic user-system feedback can reinforce popularity bias over time. HiCore learns multi-level interests with multi-channel hypergraphs and uses them across conversation and recommendation tasks, achieving reported state-of-the-art performance while facing computational and scalability limitations.
Problem
The Matthew effect is a challenge in conversational recommendation because dynamic user-system feedback can increasingly favor popular items over less-visible alternatives.
Method
HiCore constructs item-, entity-, and word-oriented triple-channel hypergraphs to learn multi-level user interests for conversational response generation and item prediction.
Results
HiCore achieves reported state-of-the-art performance across four benchmarks and effectively mitigates the Matthew effect in conversational recommendation.
Takeaways & Limitations
The results support using multi-level interests learned from multiple hypergraph channels to address popularity bias as users interact with a CRS over time.
Takeaways & Limitations
Triple-channel hypergraphs introduce computational and interpretational complexity, sparse-data concerns, and scalability, overfitting, generalizability, and resource-cost challenges.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper frames the Matthew effect as a growing challenge in conversational recommendation, where popularity bias is amplified by dynamic interaction. HiCore addresses this through multi-channel hypergraphs that learn multi-level interests for conversation and recommendation.
- Conversational recommender systems face a Matthew effect in which popular items gain visibility while less-preferred items are neglected.
- Existing work mainly targets relatively stable offline settings and overlooks the dynamic user-system feedback loop, which can intensify the problem over time.
- Single-channel hypergraphs cannot capture diverse relation patterns and may drift toward knowledge graphs when user-item interaction data are scarce.
- HiCore builds item-, entity-, and word-oriented triple-channel hypergraphs to learn item-, entity-, and word-level user interests.
- HiCore uses learned multi-level interests to support both next-utterance generation and interested-item prediction in CRS conversations.
- The authors report state-of-the-art performance across four benchmarks and effectiveness in mitigating the Matthew effect.
2 Related Work
Related work studies conversational recommendation as a dialogue-based alternative to static and interactive recommendation, while addressing user-interest diversity and popularity bias. The supplied overview also presents HiCore as a framework combining multi-interest learning with conversational recommendation.
- Research on the Matthew effect follows two lines: modeling diverse user interests and balancing item exposure across categories.
- Conversational recommendation seeks users’ intentions and interests through natural-language dialogue to provide recommendations.
- The HiCore overview combines multi-hypergraph multi-interest self-supervised learning with an interested-boosted CRS.
- The supplied related-work passage notes that restricted and scarce user-item interaction data hinder methods’ ability to capture varied user interests.
3 HiCore
HiCore targets Matthew effect in conversational recommendation by learning multi-level user interests from item-, entity-, and word-oriented multi-hypergraphs. Its multi-interest representation combines triple-channel structures and self-supervised learning for recommendation and conversation tasks.
- HiCore uses Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning together with an Interest-Boosted CRS to address Matthew effect in conversational recommendation.The framework is designed for the dynamic CRS setting and aims to improve both recommendation and conversation tasks.
- Item-oriented hypergraphs use group, joint, and purchase channels to encode social relations, shared behaviors, and implicit relationships from purchasing patterns.The channels are constructed with triangular motifs that capture higher-order local structures.
- Entity-oriented hypergraphs use items from historical conversations and their k-hop DBpedia neighbors to capture shared semantic structure despite sparse user-item interactions.Each triple channel induces hyperedges through different motifs.
- Word-oriented hypergraphs use ConceptNet neighbors and triple-channel motifs to model semantic associations among important conversation keywords.The representation targets relations such as synonymy, antonymy, and co-occurrence.
- Hypergraph convolution produces item-, entity-, and word-level interests, which an attention network concatenates into the final multi-interest representation Xm.The model then applies InfoNCE self-supervised learning, using shuffled representations as negative examples and a discriminator to evaluate alignment.
4 Experiments and Analyses
HiCore is evaluated across recommendation, conversation, Matthew-effect mitigation, hyperparameter sensitivity, and ablation studies on four CRS benchmarks. Results consistently support its effectiveness, with multi-hypergraph multi-interest modeling central to the reported gains.
- Experimental Protocol: Experiments use four CRS benchmarks and compare HiCore with established recommendation, conversation, and CRS baselines.The evaluation includes REDIAL, TG-REDIAL, OpenDialKG, and DuRecDial, alongside a broad baseline set.
- Recommendation Performance: HiCore consistently outperforms comparison baselines on recommendation metrics including R@K, M@K, and N@K for K=1, 10, 50.The reported recommendation results cover REDIAL and TG-REDIAL as well as cross-domain benchmarks.
- Study on Matthew Effect: HiCore achieves the highest Coverage across datasets, while reporting the lowest Average Popularity and Long Tail Ratio values.These results are presented as evidence that the method broadens recommendation coverage and mitigates popularity-related effects.
- Hyperparameters Analysis: The analysis reports that larger embedding dimensions help, excessive hyperedges can impede convergence, lower β gives recommendation more influence, and two hypergraph-convolution layers suffice.These observations concern embedding dimension d, comparative-learning weight β, hypergraph layers N, and hyperedge threshold P.
- Ablation Studies: Removing any item-, entity-, or word-oriented hypergraph type decreases recommendation performance, supporting the contribution of each hypergraph type.The ablations remove group-, joint-, and purchase-channel hypergraphs within each orientation.
5 Conclusion
HiCore addresses the Matthew effect in conversational recommendation by capturing multi-level user interests with item-, entity-, and word-oriented triple-channel hypergraphs. Extensive experiments show that it outperforms all baselines as users interact with the system over time.
- HiCore mitigates the Matthew effect in CRS by capturing multi-level user interests through item-, entity-, and word-oriented triple-channel hypergraphs.
- Extensive experiments show that HiCore outperforms all baselines in addressing the Matthew effect during ongoing user-system interaction.
6 Limitations
Although HiCore achieves state-of-the-art performance, its triple-channel hypergraphs introduce computational, interpretational, sparsity, scalability, overfitting, generalizability, and resource-use challenges.
- Triple-channel hypergraphs may create computational complexity, interpretational difficulties, and issues with sparse data.
- Scaling triple-channel hypergraphs to larger datasets may create scalability hurdles and increase overfitting risk.
- Generalizability and resource-intensive computations remain important considerations when applying multi-channel hypergraphs.
7 Ethics Statement
The paper reports that its data come from open-access repositories and do not pose privacy concerns. The authors state that the research adheres to EMNLP ethical standards.
- The data used in the paper are sourced from open-access repositories and are reported not to pose privacy concerns.
- The authors state that their research adheres to the ethical standards set by EMNLP.