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.

我们介绍了一种新的基于代理的信息系统，以 GPT 为代表，强调了支持代理级别信息处理和适应大语言模型（LLM）为基础的代理的特征的信息系统基础设施检查。我们构建了一种称为 Rec4Agentverse 的新型推荐范式，其中包括代理项和代理推荐。Rec4Agentverse 强调代理项和代理推荐之间的协作，促进个性化信息服务，并增强传统用户推荐反馈循环之外的信息交流。此外，我们展望了 Rec4Agentverse 的演变，并将其概念化为基于代理项、代理推荐和用户之间互动和信息交流的三个阶段。通过几个 Rec4Agentverse 案例的初步研究验证了其潜在的应用价值。最后，我们讨论了未来研究的潜在问题和有前途的方向。