@inproceedings{zheng-etal-2025-hypercrs,
title = "{H}yper{CRS}: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System",
author = "Zheng, Yongsen and
Qian, Mingjie and
Wang, Guohua and
Liu, Yang and
Chen, Ziliang and
Mao, Mingzhi and
Lin, Liang and
Lam, Kwok-Yan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.292/",
doi = "10.18653/v1/2025.findings-acl.292",
pages = "5597--5608",
ISBN = "979-8-89176-256-5",
abstract = "The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the relatively static recommendation environment. Nevertheless, the filter bubble phenomenon continues to exacerbate as users interact with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (HyperCRS), aiming to burst filter bubbles by learning multi-grained user preferences during the dynamic user-system interactions via natural language conversations. HyperCRS develops Multi-Grained Hypergraph (user-, item-, and attribute-grained) to explore diverse relations and capture high-order connectivity. It employs Hypergraph-Empowered Policy Learning, which includes Multi-Grained Preference Modeling to model user preferences and Preference-based Decision Making to disrupt filter bubbles during user interactions. Extensive results on four publicly CRS-based datasets show that HyperCRS achieves new state-of-the-art performance, and the superior of bursting filter bubbles in the CRS."
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<abstract>The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the relatively static recommendation environment. Nevertheless, the filter bubble phenomenon continues to exacerbate as users interact with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (HyperCRS), aiming to burst filter bubbles by learning multi-grained user preferences during the dynamic user-system interactions via natural language conversations. HyperCRS develops Multi-Grained Hypergraph (user-, item-, and attribute-grained) to explore diverse relations and capture high-order connectivity. It employs Hypergraph-Empowered Policy Learning, which includes Multi-Grained Preference Modeling to model user preferences and Preference-based Decision Making to disrupt filter bubbles during user interactions. Extensive results on four publicly CRS-based datasets show that HyperCRS achieves new state-of-the-art performance, and the superior of bursting filter bubbles in the CRS.</abstract>
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%0 Conference Proceedings
%T HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System
%A Zheng, Yongsen
%A Qian, Mingjie
%A Wang, Guohua
%A Liu, Yang
%A Chen, Ziliang
%A Mao, Mingzhi
%A Lin, Liang
%A Lam, Kwok-Yan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zheng-etal-2025-hypercrs
%X The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the relatively static recommendation environment. Nevertheless, the filter bubble phenomenon continues to exacerbate as users interact with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (HyperCRS), aiming to burst filter bubbles by learning multi-grained user preferences during the dynamic user-system interactions via natural language conversations. HyperCRS develops Multi-Grained Hypergraph (user-, item-, and attribute-grained) to explore diverse relations and capture high-order connectivity. It employs Hypergraph-Empowered Policy Learning, which includes Multi-Grained Preference Modeling to model user preferences and Preference-based Decision Making to disrupt filter bubbles during user interactions. Extensive results on four publicly CRS-based datasets show that HyperCRS achieves new state-of-the-art performance, and the superior of bursting filter bubbles in the CRS.
%R 10.18653/v1/2025.findings-acl.292
%U https://aclanthology.org/2025.findings-acl.292/
%U https://doi.org/10.18653/v1/2025.findings-acl.292
%P 5597-5608
Markdown (Informal)
[HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System](https://aclanthology.org/2025.findings-acl.292/) (Zheng et al., Findings 2025)
ACL
- Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, and Kwok-Yan Lam. 2025. HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5597–5608, Vienna, Austria. Association for Computational Linguistics.