Large Language Model Enhanced Recommender Systems: A Survey
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910866383831040 |
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| author | Liu, Qidong Zhao, Xiangyu Wang, Yuhao Wang, Yejing Zhang, Zijian Sun, Yuqi Li, Xiang Wang, Maolin Jia, Pengyue Chen, Chong Huang, Wei Tian, Feng |
| author_facet | Liu, Qidong Zhao, Xiangyu Wang, Yuhao Wang, Yejing Zhang, Zijian Sun, Yuqi Li, Xiang Wang, Maolin Jia, Pengyue Chen, Chong Huang, Wei Tian, Feng |
| contents | Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model Enhanced Recommender Systems: A Survey Liu, Qidong Zhao, Xiangyu Wang, Yuhao Wang, Yejing Zhang, Zijian Sun, Yuqi Li, Xiang Wang, Maolin Jia, Pengyue Chen, Chong Huang, Wei Tian, Feng Information Retrieval Artificial Intelligence Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS. |
| title | Large Language Model Enhanced Recommender Systems: A Survey |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2412.13432 |