GNN-Recommender-Systems

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GNN-Recommender-Systems is a Research & Education tool that indexes recommendation algorithms based on Graph Neural Networks. It provides a comprehensive collection of resources for researchers and developers in the field.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is GNN-Recommender-Systems?

GNN-Recommender-Systems is a valuable resource for researchers and developers focused on recommendation algorithms utilizing Graph Neural Networks (GNNs). This index compiles a wide array of GNN-based recommendation algorithms, categorized by different recommendation stages (matching, ranking, re-ranking), scenarios (social, sequential, session, bundle, cross-domain), and objectives (multi-behavior, diversity, explainability, fairness). Each entry includes the algorithm's name, associated paper, publication venue, year, and often a link to its code implementation. The project is based on a survey paper published in ACM Transactions on Recommender Systems, offering a structured overview of the field.

Best used for

Ideal for professors and researchers who need to conduct literature reviews on GNN-based recommender systems, identify specific algorithms for their projects, and find associated code implementations. Especially valuable for understanding the current landscape and advancements in the field.

Common actions

explore algorithms

find research papers

discover code implementations

understand GNN applications

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Capabilities

Key features

Index GNN recommendation algorithms
Categorize by recommendation stage
Categorize by recommendation scenario
Categorize by recommendation objective
Link to research papers
Provide code links

Target Audience

professorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of recommendation algorithms are indexed in GNN-Recommender-Systems?

The index covers recommendation algorithms based on Graph Neural Networks, categorized by different stages like matching, ranking, and re-ranking. It also includes algorithms for various scenarios such as social, sequential, session, bundle, and cross-domain recommendations, as well as those addressing objectives like multi-behavior, diversity, explainability, and fairness.

Does GNN-Recommender-Systems provide code for the listed algorithms?

Yes, for many of the indexed algorithms, GNN-Recommender-Systems provides links to their code implementations. This allows researchers and developers to easily access and experiment with the algorithms discussed in the associated papers.

Is GNN-Recommender-Systems based on a published survey paper?

Yes, this index is based on the survey paper "A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions," which was accepted by ACM Transactions on Recommender Systems (TORS). A preprint of the survey is available on arXiv.

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