Prospect Personalized Recommendation on Large Language Model-based Agent Platform
This work addresses the need for personalized recommendation systems in emerging LLM-based Agent platforms, such as GPTs, to improve information processing and interactivity, though it appears incremental as it builds on existing concepts.
The paper introduces Rec4Agentverse, a novel recommendation paradigm for LLM-based Agent platforms that emphasizes collaboration between Agent Items and Agent Recommender to enhance personalized information services and information exchange beyond traditional feedback loops. A preliminary study validates its significant potential for application.
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.