This repository is a curated list of academic papers related to recommender systems, covering a wide range of subfields and recent advancements. It serves as a valuable resource for researchers, students, and practitioners looking to stay updated on the latest trends and techniques in recommender system research.

How It Works

The repository organizes papers by conference (e.g., SIGIR, KDD, RecSys) and by specific research topics within recommender systems. This structured approach allows users to easily navigate and discover relevant literature across various areas such as collaborative filtering, sequential recommendations, knowledge-aware systems, fairness, and more.

Quick Start & Requirements

• Access: No installation required. Browse the repository directly on GitHub.
• Requirements: A web browser and internet access.

Highlighted Details

• Comprehensive coverage of major recommender system conferences from 2019 to 2022.
• Categorization by numerous sub-topics, including advanced areas like causal inference, federated learning, and graph neural networks in recommendations.
• Includes papers on crucial aspects like fairness, privacy, explainability, and robustness against attacks.
• Features a broad spectrum of recommendation tasks, from traditional collaborative filtering to specialized areas like music, news, and POI recommendations.

Maintenance & Community

• Maintained by RUCAIBox.
• Community engagement can be fostered through GitHub issues and pull requests for suggestions or additions.

Licensing & Compatibility

• The repository itself is typically licensed under a permissive license (e.g., MIT), allowing for broad use and contribution.
• Individual papers retain their original publication licenses.

Limitations & Caveats

This is a curated list of papers and does not provide code implementations or direct access to the research content itself; users will need to find the full papers through academic search engines or publisher websites.