Zhang et al. introduces Rec4Agentverse, a conceptual framework for next-generation recommender systems, in their paper Prospect Personalized Recommendation on Large Language Model-based Agent Platform. It emphasizes the interplay between Agent Items and an Agent Recommender, facilitating personalized experiences and enhancing user-agent interaction.

• Envisions a new kind of recommendation system, Rec4Agentverse
• Focuses on collaboration between Agent Items and Recommender
• Stages the evolution based on interactivity and information exchange
• Conducts a preliminary study showcasing the system’s application potential

The proposed Rec4Agentverse stands as a promising evolution in recommendation systems, advocating for a model that fosters deeper interactivity and personalization. This framework aims to challenge the conventional approach towards recommendations, paving the way for a future of collaboration between AI agents and users for enhanced information services.