LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges

Sarama Shehmir and Rasha Kashef ()
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Sarama Shehmir: Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Rasha Kashef: Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada

Future Internet, 2025, vol. 17, issue 6, 1-37

Abstract: The synthesis of large language models (LLMs) and recommender systems has been a game-changer in tailored content onslaught with applications ranging from e-commerce, social media, and education to health care. This survey covers the usage of LLMs for content recommendations (LLM4Rec). LLM4Rec has opened up a whole set of challenges in terms of scale, real-time processing, and data privacy, all of which we touch upon along with potential future directions for research in areas such as multimodal recommendations and reinforcement learning for long-term engagement. This survey combines existing developments and outlines possible future developments, thus becoming a point of reference for other researchers and practitioners in developing the future of LLM-based recommendation systems.

Keywords: large language models (LLMs); recommendation systems; LLM4Rec; generative models; discriminative models; Transformer architecture; fine-tuning; prompt tuning; recommender evaluation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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