# Recommender Systems in the Era of Large Language Models (LLMs)

**Authors:** Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, Qing Li  
**arXiv:** [2307.02046](https://arxiv.org/abs/2307.02046)  
**Date:** 2023-07-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.02046v1/

## Key points

- Recommender Systems (RecSys) are crucial in e-commerce and web applications for providing personalized suggestions based on user preferences.
- Deep Neural Networks (DNNs) have improved recommender systems by modeling user-item interactions and incorporating textual side information but face challenges such as understanding users' interests, capturing textual data effectively, generalizing recommendations, and reasoning on predictions.
- Large Language Models (LLMs) like ChatGPT and GPT4 have transformed Natural Language Processing (NLP) and AI fields with exceptional language understanding, generation capabilities, generalization skills, and reasoning abilities.
- Recent studies explore using LLMs to enhance recommender systems due to their potential.
- A comprehensive review is needed to address the evolving landscape of LLM-empowered recommender systems focusing on Pre-training, Fine-tuning, and Prompting techniques.
- Methods include utilizing LLMs as feature encoders for learning user and item representations, pre-training, fine-tuning processes, and prompting strategies to improve recommender systems.
- The paper offers insights into future directions in this field while emphasizing the importance of summarizing existing LLM-powered recommender systems for researchers in relevant domains.

## Summary

In the era of e-commerce and web applications, Recommender Systems (RecSys) have become indispensable tools in providing personalized suggestions tailored to individual user preferences. While Deep Neural Networks (DNNs) have significantly advanced recommender systems by modeling user-item interactions and incorporating textual side information, they still face challenges such as understanding users' interests, capturing textual data effectively, generalizing to diverse recommendation scenarios, and reasoning on their predictions. The emergence of Large Language Models (LLMs) like ChatGPT and GPT4 has revolutionized Natural Language Processing (NLP) and Artificial Intelligence (AI) fields with their exceptional language understanding, generation capabilities, generalization skills, and reasoning abilities. Recent studies have explored leveraging LLMs to enhance recommender systems due to their promising potential. To address the evolving landscape of LLM-empowered recommender systems, a comprehensive review is essential. This paper delves into various aspects including Pre-training, Fine-tuning, and Prompting techniques. Initially introducing methods that utilize LLMs as feature encoders for learning user and item representations, the review then explores recent advancements in leveraging LLMs through pre-training, fine-tuning processes, and prompting strategies to enhance recommender systems. The authors provide insights into future directions in this emerging field while highlighting the importance of summarizing existing LLM-empowered recommender systems for researchers in relevant domains. By offering an in-depth understanding of how LLMs can be harnessed to improve recommendation algorithms,this review aims to contribute to the ongoing evolution of recommender systems powered by cutting-edge language models.