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

• URL: http://arxiv.org/abs/2502.13843v2
• Date: Fri, 18 Apr 2025 07:48:48 GMT
• Title: AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations
• Authors: Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu,
• Abstract summary: LLM-based user agents are emerging as a promising approach to enhancing recommender systems.<n>We propose a dual-layer memory architecture combined with a two-step fusion mechanism.<n>We also introduce the concepts of interest groups and group-shared memory to better capture the influence of popularity factors on users with similar interests.
• Score: 28.559223475725137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. This design avoids irrelevant information during decision-making while ensuring effective integration of cross-domain preferences. We also introduce the concepts of interest groups and group-shared memory to better capture the influence of popularity factors on users with similar interests. Comprehensive experiments validate the effectiveness of AgentCF++. Our code is available at https://github.com/jhliu0807/AgentCF-plus.

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