FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System
The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information or content that reinforces their existing dominant preferences and beliefs. This results in a lack of exposure to diverse and varied content. Many existing works have predominantly examined filter bubbles in static or relatively-static recommendation settings. However, filter bubbles will be continuously intensified over time due to the feedback loop between the user and the system in the real-world online recommendation. To address these issues, we propose a novel paradigm, Multi-Facet Preference Learning for Pricking F...
| Authors | Yongsen Zheng, Ziliang Chen, Jinghui Qin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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