A Survey on LLM-powered Agents for Recommender Systems

• URL: http://arxiv.org/abs/2502.10050v1
• Date: Fri, 14 Feb 2025 09:57:07 GMT
• Title: A Survey on LLM-powered Agents for Recommender Systems
• Authors: Qiyao Peng, Hongtao Liu, Hua Huang, Qing Yang, Minglai Shao,
• Abstract summary: Large Language Model (LLM)-powered agents offer a promising approach by enabling natural language interactions and interpretable reasoning.<n>This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems.
• Score: 16.463945811669245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.

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