The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agentverse emphasizes the collaboration between Agent Items and Agent Recommender, thereby promoting personalized information services and enhancing the exchange of information beyond the traditional user-recommender feedback loop. Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user. A preliminary study involving several cases of Rec4Agentverse validates its significant potential for application. Lastly, we discuss potential issues and promising directions for future research.

翻译：以GPTs为代表的新型面向智能体的信息系统，促使我们审视支撑智能体层面信息处理的信息系统基础设施，并适应基于大语言模型(LLM)智能体的交互性等特征。本文展望了基于LLM智能体平台的推荐系统前景，提出了一种名为Rec4Agentverse的新型推荐范式，该范式由智能体物品和智能体推荐器组成。Rec4Agentverse强调智能体物品与智能体推荐器之间的协作，从而促进个性化信息服务并增强传统用户-推荐器反馈循环之外的信息交换。此外，我们展望了Rec4Agentverse的演进过程，并基于智能体物品、智能体推荐器与用户之间交互与信息交换的增强程度，将其概念化为三个阶段。通过涵盖Rec4Agentverse多个案例的初步研究，验证了其显著的应用潜力。最后，我们探讨了潜在问题及未来研究中富有前景的方向。