Prospect Personalized Recommendation on Large Language Model-based Agent Platform
CoRR(2024)
摘要
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.
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