A survey on large language models for recommendation

By Likang Wu, Zhilan Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu and 2 more. World wide web (Bussum), 2023

899 citations recorded by the source corpus.

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning, prompt tuning, etc. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers and resources on LLMs for recommendation (https://github.com/WLiK/LLM4Rec-Awesome-Papers).

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b486982fa7c68a8a08df1111ba9607119419c488

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. Wayne Xin Zhao
2. Ji-rong Wen
3. Yupeng Hou
4. Hongyu Lu
5. Bowen Zheng
6. Yu Chen
7. Ruobing Xie
8. Junjie Zhang

In Information Systems

1. Wayne Xin Zhao
2. Yupeng Hou
3. Ji-rong Wen
4. Hongyu Lu
5. Bowen Zheng
6. Yu Chen
7. Ruobing Xie
8. Junjie Zhang

Citations in the corpus

• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, background): “As the scaling law shows, the performance of LLMs on various downstream tasks generally increases while scaling up the model size and the amount of training data [24, 15, 28].”
• When large language models meet personalization: perspectives of challenges and opportunities (2023, positive): “tive of learning mechanisms, LLM4Rec [5] delves into both Discriminative LLM for Recommendation and Generative LLM for Recommendation.”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, neutral): “Wu et al. [19] summarize the LLMs for recommender systems from discriminative and generative perspectives.”
• 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.”
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, positive): “Inspired by Su et al. [32], we categorize user intentions into three types according to the varying degree of clarity.”
• How Can Recommender Systems Benefit from Large Language Models: A Survey (2023, neutral): “In Section 4, we highlight five key challenges for the adaption of LLM to RS from three aspects ( i.e. , efficiency , effectiveness , and ethics ), which mainly arise from the unique characteristics of recommender systems especially in industrial applications.”
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023, background): “Specifically, there are also several attempts [17] that adapt LLMs for recommender systems (RS), to improve the item ranking performance [18] or boost the comprehensive recommendation capacities [19].”
• Hard Negative Sampling via Large Language Models for Recommendation (2025, unknown): “Existing LLM-based recommendation algorithms can be divided into two categories (Wu et al., 2024b; Li et al., 2023c; Zhao et al., 2023b): us-ing LLMs as recommenders and using LLMs to enhance traditional recommendations.”