A Survey on LLM -powered Agents for Recommender Systems (2025)

Abstract: Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation, prompting the recommendation community to leverage these powerful models to address fundamental challenges in traditional recommender systems, including limited comprehension of complex user intents, insufficient interaction capabilities, and inadequate recommendation interpretability. This survey presents a comprehensive synthesis of this rapidly evolving field. We consolidate existing studies into three paradigms: (i) recommender-oriented methods, which directly enhance core recommendation mechanisms; (ii) interaction-oriented methods, which conduct multi-turn conversations to elicit preferences and deliver interpretable explanations; and (iii) simulation-oriented methods, that model user-item interactions through multi-agent frameworks. Then, we dissect a four-module agent architecture: profile, memory, planning, and action. Then we review representative designs, public datasets, and evaluation protocols. Finally, we give the open challenges that impede real-world deployment, including cost-efficient inference, robust evaluation, and security.

## introduction in the era of information explosion, recommender systems have become an indispensable component of digital platforms, helping users navigate through massive amounts of content across various domains. while traditional recommendation approaches @xcite have achieved considerable success in providing personalized recommendations, they still face significant challenges, such as limited understanding of complex user intents, insufficient interaction capabilities, and the inability to provide interpretable recommendations @xcite . ## methods in this section, we sort out existing llm-powered agent recommendation works based on the overall objective of the method and the agent components of different methods. ## future directions refinement of evaluation framework there is a notable absence of unified and comprehensive evaluation standards for accurately measuring dialogue quality and recommendation effectiveness. future research necessitates the establishment of robust evaluation frameworks, development of novel performance metrics, and consideration of privacy and security concerns in practical applications. @xcite reveals the vulnerability of llm-empowered recommender systems to adversarial attacks. in future, the researchers could develop robust adversarial detection methods, investigate multi-agent defensive architectures, and integrating domainspecific security knowledge into defense. ## conclusion recent, the integration of llm-powered agents into recommender systems has emerged as a significant advancement. in this survey, we established a systematic taxonomy categorizing existing approaches into three paradigms: recommenderoriented, interaction-oriented, and simulationoriented. we analyzed these methods through a comprehensive four-module architectural framework and critically examined the datasets and evaluation methodologies employed across the literature. finally, we identify two promising directions for future exploration.