Prospect Personalized Recommendation on Large Language Model-based Agent Platform

TL;DR

This paper introduces Rec4Agentverse, a novel recommendation paradigm for LLM-based agent platforms that enhances personalized information exchange through collaboration between Agent Items and Recommenders, with promising preliminary validation.

Contribution

It proposes Rec4Agentverse, a new recommendation framework tailored for LLM-based agent systems, emphasizing collaboration and interaction enhancement.

Findings

Preliminary cases validate Rec4Agentverse's potential.

The paradigm promotes personalized information services.

It outlines future research directions.

Abstract

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.