发表机构
Sun Yat-sen University; Jinan University; Guangdong University of Technology（中山大学; 暨南大学; 广东工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对推荐系统中过滤气泡随时间加剧的问题，提出FacetCRS框架，通过多面偏好学习建模用户动态偏好，在对话推荐系统中实现端到端推荐，实验证明其有效缓解过滤气泡并提升推荐质量。
AI 中文摘要
过滤气泡是推荐系统（RSs）中一个臭名昭著的问题，它描述了用户被暴露于有限且狭窄的信息或内容范围的现象，这些内容强化了他们现有的主导偏好和信念，导致用户缺乏接触多样化和多变内容的机会。许多现有工作主要研究了静态或相对静态推荐设置中的过滤气泡。然而，在现实世界的在线推荐中，由于用户与系统之间的反馈循环，过滤气泡会随着时间的推移不断加剧。为了解决这些问题，我们提出了一种新颖的范式，即对话推荐系统中用于戳破过滤气泡的多面偏好学习（FacetCRS），旨在通过自然语言对话的及时用户-项目交互来打破对话推荐系统（CRS）中的过滤气泡。通过考虑多样化的用户偏好和意图，FacetCRS自动将用户偏好建模为多个方面，包括实体面、词面、上下文面和评论面，以捕捉多样化和动态的用户偏好，从而戳破CRS中的过滤气泡。它是一个端到端的CRS框架，能够自适应地学习不同级别的偏好面表示和多种类型的外部知识。在两个公开可用的基准数据集上进行的大量实验表明，我们提出的方法在缓解过滤气泡和提升CRS推荐质量方面达到了最先进的性能。
英文摘要
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 Filter Bubbles in Conversational Recommender System (FacetCRS), which aims to burst filter bubbles in the conversational recommender system (CRS) through timely user-item interactions via natural language conversations. By considering diverse user preferences and intentions, FacetCRS automatically model user preference into multi-facets, including entity-, word-, context-, and review-facet, to capture diverse and dynamic user preferences to prick filter bubbles in the CRS. It is an end-to-end CRS framework to adaptively learn representations of various levels of preference facet and diverse types of external knowledge. Extensive experiments on two publicly available benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance in mitigating filter bubbles and enhancing recommendation quality in CRS.
