用生成式AI打造会对话的群组推荐助手，让多人决策更自然。
Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support
- 设计可主动交互的AI代理，支持群组在聊天中协同决策。
- 提出从单次推荐转向智能协作的新型系统范式。
- 适合希望提升群体决策效率的研究者与产品设计者。
二十多年来，学术界提出了多种面向群体用户的推荐算法，涵盖偏好获取、聚合及推荐生成等环节。然而，现实中几乎找不到真正落地的群组推荐系统。这引发我们对现有研究假设的反思，特别是关于群体沟通方式和推荐如何辅助决策的理解。本文主张重新思考该领域方向，利用现代生成式AI（如ChatGPT）的能力。我们设想未来的群组推荐系统应是人类成员通过聊天互动，由一个具备主动性（agentic）的AI推荐代理协助决策过程。这一模式有望构建更自然的群体决策环境，推动群组推荐系统在实际应用中的普及。
原文摘要 · Abstract (English)
More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a rich variety of algorithmic proposals were published, e.g., on how to acquire individual preferences, how to aggregate them, and how to generate recommendations for groups of users. However, despite the rich literature on the topic, barely any examples of real-world group recommender systems can be found. This lets us question common assumptions in academic research, in particular regarding communication processes in a group and how recommendation-supported decisions are made. In this essay, we argue that these common assumptions and corresponding system designs often may not match the needs or expectations of users. We thus call for a reorientation in this research area, leveraging the capabilities of modern Generative AI assistants like ChatGPT. Specifically, as one promising future direction, we envision group recommender systems to be systems where human group members interact in a chat and an AI-based group recommendation agent assists the decision-making process in an agentic way. Ultimately, this shall lead to a more natural group decision-making environment and finally to wider adoption of group recommendation systems in practice.
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