Towards LLM-Enhanced Group Recommender Systems
This addresses the problem of improving group recommendations for users in collaborative settings, but it appears incremental as it focuses on analyzing existing LLMs rather than introducing a new method.
The paper tackles the complexities of group recommender systems, such as group dynamics and decision-making, by analyzing how large language models (LLMs) can enhance decision support quality and applicability.
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.