用大模型提升群组推荐的决策质量与解释能力
Towards LLM-Enhanced Group Recommender Systems
- 利用大语言模型理解群组动态和成员关系
- 实现兼顾全体成员需求的推荐结果
- 为群组及个人提供可解释的推荐依据
与面向单个用户的推荐系统不同，群组推荐系统旨在为群体生成并解释推荐内容。这种群组导向的场景引入了额外复杂性，需解决多个在个体情境中不存在的问题，如理解群组内部的社会依赖关系、定义有效的决策机制、确保推荐对所有成员都合适，以及提供群组级解释和针对个体的解释。本文分析大语言模型（LLMs）如何支持这些方面，从而提升群组推荐系统的整体决策支持质量和应用可行性。
原文摘要 · Abstract (English)
In contrast to single-user recommender systems, group recommender systems are designed to generate and explain recommendations for groups. This group-oriented setting introduces additional complexities, as several factors - absent in individual contexts - must be addressed. These include understanding group dynamics (e.g., social dependencies within the group), defining effective decision-making processes, ensuring that recommendations are suitable for all group members, and providing group-level explanations as well as explanations for individual users. In this paper, we analyze in which way large language models (LLMs) can support these aspects and help to increase the overall decision support quality and applicability of group recommender systems.
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