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

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 o...

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Bibliographic Details
Main Authors: Sarama Shehmir, Rasha Kashef
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Future Internet
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Online Access:https://www.mdpi.com/1999-5903/17/6/252
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Summary: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.
ISSN:1999-5903