H yper CRS : Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025)

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.

## introduction conversational recommendation systems (crss) @xcite are powerful tools that enable the provision of personalized recommendations to assist users in finding desirable items, which have been widely applied in diverse domains such as e-commerce @xcite , music recommendation @xcite ). ## hypercrs filter bubbles is a notorious issue in the crs, and it will be increasingly intensified over time as users interact with the system. to address these issues, we propose a novel paradigm, hypercrs, which consists of multi-grained hypergraph construction and hypergraph-aware policy learning. the overall pipeline of hypercrs is depicted in fig. 2 . ## experiments and analyses we conduct experiments on four crs-based benchmarks to answer the following questions: ## conclusion to break filter bubbles in the crs, we propose hy-percrs, which features multi-grained hypergraph and hypergraph-aware policy learning. the former models multiplex relations through high-order interactions among entities, while the latter learns diverse preferences via hypergraph structures and sequential information. extensive experimental results validate the effectiveness of hypercrs, demonstrating its superiority in breaking filter bubbles within crs, providing users with a broader spectrum of options and fostering a more enriching online experience. in future work, we plan to integrate large language model (llm) agents into hypercrs to enhance conversational capabilities and improve user interactions, and we will also focus on developing user-centric evaluation metrics that assess the effectiveness of llm interactions within the recommendation framework.