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HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin
TL;DR
The paper addresses the Matthew effect in conversational recommendation, where dynamic interaction can favor popular items and overlook less popular ones. HyCoRec learns multi-aspect preferences with hypergraph-enhanced components, and experiments on two benchmarks report superior performance and Matthew-effect mitigation, although review-based hypergraph construction and separate hypergraph designs remain limitations.
Problem
The Matthew effect favors popular items while less popular items are overlooked, and prior methods mainly address static settings rather than dynamic conversational interaction.
Method
HyCoRec combines hypergraph-enhanced multi-preference learning with a hypergraph-aware CRS to model item-, entity-, word-, review-, and knowledge-aspect preferences.
Results
HyCoRec outperforms compared baselines on two benchmarks and achieves the highest Coverage and lowest isolation-index values across datasets.
Takeaways & Limitations
HyCoRec provides a conversational recommendation framework that reports broader recommendation coverage while alleviating the Matthew effect during user interaction.
Takeaways & Limitations
The current method omits a review-based hypergraph because complex item reviews make its construction challenging and requires separately designed hypergraphs for different preference aspects.
Abstract
from arXiv · showhide
The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{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, \emph{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. Our code is available at https://github.com/zysensmile/HyCoRec.
1 Introduction
Conversational recommender systems face a Matthew effect that favors popular items while overlooking less popular ones, and this problem can intensify through dynamic user-system interaction. HyCoRec addresses these limitations by learning multi-aspect preferences for conversational response generation and item prediction.
- The Matthew effect exposes popular items more often while less popular items tend to be overlooked or ignored.
- Prior work mainly examines the Matthew effect in static or relatively static offline recommendation settings.
- Dynamic user-system interaction can progressively amplify the Matthew effect, contributing to filter bubbles and echo chambers.
- Pairwise knowledge-graph edges restrict preference learning, whereas user preferences may involve multiple factors simultaneously.
- HyCoRec learns item-, entity-, word-, review-, and knowledge-aspect preferences using hypergraphs, reviews, and knowledge graphs.
- HyCoRec is presented as an end-to-end framework for generating conversational responses and predicting recommendation items.
- Experiments on two CRS-based datasets report superior performance and effectiveness in mitigating the Matthew effect.
2 Related Work
Related work studies the Matthew effect, recommendation diversity, and popularity bias, but predominantly addresses static recommendation rather than the dynamic feedback loop in conversational recommendation.
- Conversational recommender systems capture preferences through dialogue and provide recommendations, with research commonly divided into attribute-based and generation-based approaches.
- The Matthew effect has been documented in recommender systems, including YouTube and collaborative-filtering settings.
- Existing mitigation strategies emphasize recommendation diversity and removing popularity bias.
- These methods predominantly study static settings, whereas HyCoRec considers the dynamic user-system feedback loop.
3 HyCoRec
HyCoRec targets the Matthew effect in conversational recommendation by combining hypergraph-enhanced multi-preference learning with a hypergraph-aware conversational recommender system.
- The Matthew effect intensifies over time through the dynamic user-system feedback loop in conversational recommendation.
- HyCoRec consists of Hypergraph-Enhanced Multi-Preference Learning and Hypergraph-Aware CRS.
3.1 Preliminaries
Conversational recommendation uses continuous dialogue to understand user preferences and provide customized suggestions. HyCoRec represents user inclinations with multi-grained hypergraphs containing nodes, hyperedges, and weighted node-hyperedge links.
- Conversational recommendation uses continuous dialogue to gather users’ preferences, context, and requirements for customized suggestions.
- HyCoRec constructs item-based, entity-based, and word-based hypergraphs to represent user inclinations.
- Each hypergraph is described using a node collection, a hyperedge ensemble, and weighted links between nodes and hyperedges.
3.2 Hypergraph-Enhanced Multi-Preference Learning
HyCoRec constructs multiple hypergraphs and combines them with reviews and knowledge graphs to learn diverse item-, entity-, word-, review-, and knowledge-aspect user preferences.
- HyCoRec formulates multi-hypergraph construction and multi-preference learning to model diverse user preferences for mitigating the Matthew effect.
- Traditional knowledge graphs capture only pairwise interactions, whereas hypergraphs connect more than two vertices to represent complex item relations.
- Item-based Hypergraph: The item-based hypergraph extracts session items as vertices, forms hyperedges, and connects user-associated hyperedges through shared items.
- Entity-based Hypergraph: The entity-based hypergraph uses DBpedia items and their k-hop neighbors to form hyperedges that capture shared semantic connotations.
- Word-based Hypergraph: The word-based hypergraph uses conversation keywords and ConceptNet relations, extending each keyword with k-hop neighbors into hyperedges linked by shared words.
- Multi-Preference Learning: Multi-preference learning derives item-, entity-, word-, review-, and knowledge-aspect preferences from hypergraphs, Transformer-encoded reviews, and graph-encoded conversation entities.
3.3 Hypergraph-Aware CRS
Hypergraph-Aware CRS integrates multiple preference representations separately for recommendation and conversation generation, supporting item prediction and diverse response generation.
- HyCoRec adopts Pi, Pe, Pw, Pr, and Pc to predict items and generate responses in conversational recommendation.
- Recommendation Task: The recommendation task concatenates multiple preferences into Pmulrec, which scores candidate items from the item set and is trained with cross-entropy loss.
- Conversational Task: The conversation task fuses multi-aspect preferences into Pmulcon and feeds it into a Transformer encoder-decoder to generate diverse responses.
- Conversational Task: Preference-aware and item-related biases are used alongside vocabulary, vocabulary-bias, and copy probabilities to enhance response diversity.
4 Experiments and Analyses
Experiments on REDIAL and TG-REDIAL evaluate HyCoRec across recommendation, conversation, Matthew-effect mitigation, ablations, hyperparameters, and case studies. HyCoRec outperforms compared methods and produces more diverse recommendations and responses.
- Experimental Setup: HyCoRec is evaluated on REDIAL and TG-REDIAL across recommendation and conversational tasks.The experiments address recommendation performance, conversation quality, Matthew-effect mitigation, component contributions, hyperparameters, and qualitative cases.
- Recommendation Performance: HyCoRec outperforms all compared methods on the recommendation task under Recall@K, MRR@K, and NDCG@K.The evaluation uses K=10 and 50, with Table 1 reporting the recommendation results.
- Conversational Performance: HyCoRec is superior to all compared baselines in conversational response diversity measured by Distinct ngram for n=2,3,4.The baseline ranking is KBRD, KGSF, Transformer, then ReDial.
- Matthew-Effect Mitigation: HyCoRec achieves the highest Coverage and lowest Isolation-Index across datasets compared with the strongest baselines.On REDIAL, Cover@5 improves by 102.72%, 75.90%, 144.35%, and 63.75% over KBRD, KGSF, KGConvRec, and MHIM, respectively.
- Ablation Studies: Removing any item, entity, word, or review component causes a substantial performance decline, supporting the contribution of multi-aspect knowledge data.The ablations test variants without each hypergraph or item reviews.
- Hyperparameters Analysis: Recommendation performance continually improves with embedding dimension, peaks at two hypergraph-convolution layers, and can decline with excessive pooling layers.The reported explanation is that larger convolution depth may overfit, while excessive pooling may lose important representations.
- Case Studies: Case studies show HyCoRec recommending movies across different categories, whereas most existing methods typically recommend movies within the same category.Different colors denote categories in one view, while the same color denotes the same category in another.
5 Conclusion
HyCoRec addresses the Matthew effect in conversational recommendation by learning five types of user preferences and improves both conversational responses and item prediction. Experiments validate superior performance and effectiveness in alleviating the Matthew effect.
- HyCoRec learns item-, entity-, word-, review-, and knowledge-aspect preferences to alleviate the Matthew effect in conversational recommendation.
- HyCoRec aims to generate diverse conversational responses and accurately predict recommended items.
- Experiments show that HyCoRec outperforms all compared baselines and alleviates the Matthew effect in conversational recommendation.
6 Limitations
HyCoRec has limitations in constructing review-based hypergraphs and in requiring separate hypergraphs for different preference aspects.
- The complexity and extensive nature of item reviews makes constructing a review-based hypergraph challenging, so the current version omits it.
- HyCoRec requires individually designed hypergraphs for learning multi-aspect preferences.
- A general framework that integrates arbitrary hypergraph types could automatically unify diverse knowledge sources.
7 Ethics Statement
The study uses data from open-access repositories and reports no privacy concerns, stating that the research adheres to ACL ethical standards.
- The study uses data sourced from open-access repositories and reports no privacy concerns.
- The authors state that their research adheres to ACL ethical standards.