This repository is a curated list of academic papers focusing on Large Language Models (LLMs) applied to Recommender Systems (LLM4RS). It serves researchers and practitioners interested in leveraging LLMs for enhanced recommendation tasks, offering a comprehensive overview of recent advancements, methodologies, and datasets in this rapidly evolving field.

How It Works

The repository compiles research papers, categorizing them into surveys, specific LLM4RS applications, pre-training strategies, and relevant datasets. It highlights various approaches, including using LLMs for zero-shot ranking, instruction following, generative recommendations, conversational systems, and integrating collaborative filtering with LLMs. The collection aims to provide a structured entry point into the LLM4RS landscape.

Quick Start & Requirements

This is a curated list of papers and does not involve direct installation or execution. Users can access the listed papers via provided links (e.g., arXiv, conference proceedings).

Highlighted Details

• Extensive coverage of papers from 2023-2024, reflecting the field's recent surge.
• Includes links to both papers and associated code repositories where available.
• Features a dedicated section for datasets relevant to LLM4RS research.
• Categorizes papers into surveys, specific applications, and foundational techniques.

Maintenance & Community

The repository is maintained by nancheng58 and welcomes contributions via issues and pull requests.

Licensing & Compatibility

The repository itself is not software and thus not licensed. Individual papers retain their original publication licenses.

Limitations & Caveats

This is a bibliography and does not provide code for direct implementation or benchmarking. Users must independently access and evaluate the cited research.