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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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/6/252 |
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| author | Sarama Shehmir Rasha Kashef |
| author_facet | Sarama Shehmir Rasha Kashef |
| author_sort | Sarama Shehmir |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f0bfa586b0b49f19d5bb5d920c9aaff |
| institution | Matheson Library |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-2f0bfa586b0b49f19d5bb5d920c9aaff2025-06-25T13:52:38ZengMDPI AGFuture Internet1999-59032025-06-0117625210.3390/fi17060252LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and ChallengesSarama Shehmir0Rasha Kashef1Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaElectrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaThe 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.https://www.mdpi.com/1999-5903/17/6/252large language models (LLMs)recommendation systemsLLM4Recgenerative modelsdiscriminative modelsTransformer architecture |
| spellingShingle | Sarama Shehmir Rasha Kashef LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges Future Internet large language models (LLMs) recommendation systems LLM4Rec generative models discriminative models Transformer architecture |
| title | LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges |
| title_full | LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges |
| title_fullStr | LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges |
| title_full_unstemmed | LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges |
| title_short | LLM4Rec: A Comprehensive Survey on the Integration of Large Language Models in Recommender Systems—Approaches, Applications and Challenges |
| title_sort | llm4rec a comprehensive survey on the integration of large language models in recommender systems approaches applications and challenges |
| topic | large language models (LLMs) recommendation systems LLM4Rec generative models discriminative models Transformer architecture |
| url | https://www.mdpi.com/1999-5903/17/6/252 |
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