Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents

• URL: http://arxiv.org/abs/2502.13843v1
• Date: Wed, 19 Feb 2025 16:02:59 GMT
• Title: Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents
• Authors: Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu,
• Abstract summary: Large Language Model (LLM)-based user agents have emerged as a powerful tool for improving recommender systems. We introduce AgentCF++, a novel framework featuring a dual-layer memory architecture and a two-step fusion mechanism.
• Score: 28.559223475725137
• License:
• Abstract: Large Language Model (LLM)-based user agents have emerged as a powerful tool for improving recommender systems by simulating user interactions. However, existing methods struggle with cross-domain scenarios due to inefficient memory structures, leading to irrelevant information retention and failure to account for social influence factors such as popularity. To address these limitations, we introduce AgentCF++, a novel framework featuring a dual-layer memory architecture and a two-step fusion mechanism to filter domain-specific preferences effectively. Additionally, we propose interest groups with shared memory, allowing the model to capture the impact of popularity trends on users with similar interests. Through extensive experiments on multiple cross-domain datasets, AgentCF++ demonstrates superior performance over baseline models, highlighting its effectiveness in refining user behavior simulation for recommender systems. Our code is available at https://anonymous.4open.science/r/AgentCF-plus.

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