LLM-based agents are transforming recommender systems by introducing a new paradigm, the Rec4Agentverse, which integrates intelligent and interactive Item Agents. These agents offer personalized recommendations and improve user interactions through natural language processing and dialogue. The evolution of the Rec4Agentverse is structured in three progressive stages that enhance the exchange of information between the agent recommender, Item Agents, and users. This development necessitates an upgraded information system infrastructure that supports agent-level interactions and processing, thus expanding the potential of recommender systems significantly.
The Rec4Agentverse is a new recommendation paradigm for an LLM-based agent platform, offering personalized suggestions from Item Agents to users via the Agent Recommender.
Distinguished by their interactivity, intelligence, and proactive capabilities, Item Agents represent a significant evolution from traditional items in recommender systems.
LLM-based agents can now evolve into domain experts, becoming novel information carriers with domain-specific knowledge.
Along with the increase in LLM-based agents in various domains, agent platforms represent a new kind of information system, featuring agent-oriented information gathering, storage, and exchange.
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