This repository serves as a comprehensive, curated bibliography of academic papers and tutorials focused on Recommender Systems (RS). It targets researchers, engineers, and students in the field, offering a structured overview of seminal works and recent advancements across various RS sub-domains, including deep learning, social influence, cold-start problems, and emerging areas like LLMs for RS.

How It Works

The repository organizes papers into distinct categories, such as Tutorials, Surveys, General RS, Social RS, Deep Learning-based RS, and specialized topics like LLM for RS, Privacy & Security RS, and more. Each entry typically includes author(s), paper title, and publication venue/year, providing a structured reference for literature review and research exploration.

Quick Start & Requirements

This repository is a curated list of papers and does not involve code execution or installation. It serves as a knowledge base.

Highlighted Details

• Extensive coverage of 17 specialized sub-domains within Recommender Systems.
• Includes recent additions on LLMs for RS and RS Tutorials.
• Features a contribution template for community additions.
• Lists papers from major conferences (RecSys, KDD, WWW, SIGIR, AAAI, etc.) and journals.

Maintenance & Community

The project is maintained by hongleizhang and welcomes community contributions via pull requests. Acknowledgements are given to contributors who have helped curate specific sections.

Licensing & Compatibility

The repository itself, as a collection of links and citations, is not subject to software licensing. The underlying papers are subject to their respective copyright and publication licenses.

Limitations & Caveats

This repository is a static list of papers and does not provide implementations, code, or datasets. The quality and accessibility of the papers depend on their original publication sources.