This repository serves as a curated collection of research papers and code related to Contrastive Learning (CL), Data Augmentation (DA), and Self-Supervised Learning (SSL) in the domain of Recommender Systems. It aims to provide researchers and practitioners with a comprehensive overview of the latest advancements, techniques, and frameworks in this rapidly evolving field.

How It Works

The repository categorizes papers into key areas: Surveys/Tutorials/Frameworks, Data Augmentation only, Graph Models with CL, Sequential Models with CL, and Other Tasks with CL. This structure allows users to quickly navigate to relevant research, covering a broad spectrum of recommender system applications and methodologies.

Quick Start & Requirements

This repository is a curated list of papers and does not have a direct installation or execution command. Users are expected to follow the provided links to access research papers (PDFs) and associated code repositories for specific implementations.

Highlighted Details

• Extensive coverage of over 200 papers focusing on "Other Tasks with CL".
• Detailed listings for "Graph Models with CL" (186 papers) and "Sequential Models with CL" (149 papers).
• Includes foundational surveys and frameworks for SSL in recommender systems.
• Features numerous papers specifically on Data Augmentation techniques for sequential recommendation.

Maintenance & Community

The repository welcomes comments and contributions, indicating an active effort to maintain and expand the collection. Specific contributors or community links are not detailed in the provided README.

Licensing & Compatibility

No specific licensing information is provided for the repository itself. Users should refer to the licenses of individual linked papers and code repositories.

Limitations & Caveats

This repository is a curated list and does not provide executable code or a unified framework. Users must individually access and evaluate each cited paper and its associated code. The sheer volume of papers may require significant effort to sift through for specific needs.