How Can Recommender Systems Benefit from Large Language Models: A Survey

By Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang and 1 more. ACM Trans. Inf. Syst., 2023

404 citations recorded by the source corpus.

With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for mitigating information overload and matching users’ information needs by providing personalized suggestions over items. Although the RS research community has made remarkable progress over the past decades, conventional recommendation models (CRM) still have some limitations, e.g., lacking open-domain world knowledge, and difficulties in comprehending users’ underlying preferences and motivations. Meanwhile, large language models (LLM) have shown impressive general intelligence and human-like capabilities for various natural language processing (NLP) tasks, which mainly stem from their extensive open-world knowledge, logical and commonsense reasoning abilities, as well as their comprehension of human culture and society. Consequently, the emergence of LLM is inspiring the design of RS and pointing out a promising research direction, i.e., whether we can incorporate LLM and benefit from their common knowledge and capabilities to compensate for the limitations of CRM. In this article, we conduct a comprehensive survey on this research direction, and draw a bird’s-eye view from the perspective of the whole pipeline in real-world RS. Specifically, we summarize existing research works from two orthogonal aspects: where and how to adapt LLM to RS. For the “WHERE” question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, user interaction, and pipeline controller. For the “HOW” question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLM or not during training, and whether to involve CRM for inference. Detailed analysis and general development paths are provided for both “WHERE” and “HOW” questions, respectively. Then, we highlight the key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/bac54736112098616f0e1c90435888ef3e119d32

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lianghao Xia
2. Chao Huang
3. Suqi Cheng
4. Xubin Ren
5. Wei Wei
6. Lixin Su
7. Junfeng Wang
8. Dawei Yin

In Information Systems

1. Lianghao Xia
2. Chao Huang
3. Suqi Cheng
4. Xubin Ren
5. Wei Wei
6. Lixin Su
7. Junfeng Wang
8. Dawei Yin

Citations in the corpus

• Representation Learning with Large Language Models for Recommendation (2023, result): “It is evident from the results in Figure 1 that the recommendations refined by the ChatGPT perform worse than the original results provided by LightGCN.”
• A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, neutral): “In addition, cognitive psychology has been studied for a long time, so many effective human memory theories and architectures have been accumulated, which can support more advanced capabilities of the agents [147].”
• When large language models meet personalization: perspectives of challenges and opportunities (2023, neutral): “Regarding the adaptation of LLM for recommender systems in terms of ‘Where’ and ‘How’, Li et al [6] concentrate on the overall pipeline in industrial recommender phases.”