# A Survey on Large Language Models for Recommendation

**Authors:** Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen  
**arXiv:** [2305.19860](https://arxiv.org/abs/2305.19860)  
**Date:** 2023-05-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.19860v1/

## Key points

- Large Language Models (LLMs) are powerful tools in Natural Language Processing (NLP) and Recommendation Systems (RS)
- LLMs are trained on massive amounts of data using self-supervised learning
- LLMs have shown remarkable success in learning universal representations
- LLMs can enhance recommendation systems through transfer techniques like fine-tuning and prompt tuning
- High-quality representations of textual features and extensive coverage of external knowledge are key aspects of leveraging language models in improving recommendation quality
- Two major paradigms for LLM-based recommendation systems: Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
- Existing LLM-based recommendation systems within each paradigm are reviewed and analyzed, providing insights into methodologies, techniques, and performance
- Key challenges associated with LLM-based recommendation systems are identified
- Valuable findings that can inspire researchers and practitioners in this domain are presented
- This survey serves as an essential resource for understanding the current landscape of LLM-based recommendation systems.

## Summary

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have gained significant attention in Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have shown remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems through effective transfer techniques such as fine-tuning and prompt tuning. The key aspect of leveraging language models in improving recommendation quality lies in their high-quality representations of textual features and extensive coverage of external knowledge, which allows for establishing correlations between items and users. To provide a comprehensive understanding of LLM-based recommendation systems, this survey introduces a taxonomy that categorizes these models into two major paradigms: Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec). Within each paradigm, existing LLM-based recommendation systems are reviewed and analyzed, offering insights into their methodologies, techniques, and performance. Furthermore, this survey identifies key challenges associated with LLM-based recommendation systems. It also presents several valuable findings that can inspire researchers and practitioners working in this domain. With the increasing popularity of large language models in NLP and RS fields, this survey provides an essential resource for understanding the current landscape of LLM-based recommendation systems.