AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations

Understand

LLM-based user agents, which simulate user interaction behavior, are emerging as a promising approach to enhancing recommender systems.

• In real-world scenarios, users' interactions often exhibit cross-domain characteristics and are influenced by others.
• However, the memory design in current methods causes user agents to introduce significant irrelevant information during decision-making in cross-domain scenarios and makes them unable to recognize the influence of other users' interactions, such as popularity factors.
• To tackle this issue, we propose a dual-layer memory architecture combined with a two-step fusion mechanism.