# tsinghua-fib-lab/GNN-Recommender-Systems

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

Repository: https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems
Canonical: https://ross.abutalabs.com/products/gnn-recommender-systems
License Family: other
Topics: gnn, graph-neural-networks, gcn, graph-convolutional-networks, recommendation-system, recommendation, recommendation-algorithms, recommender-system, graph-representation-learning, information-retrieval
Last push: 2022-12-17T01:33:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1805, "days_push": 1356, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1077, forks 145 (observed 2026-08-28T04:03:29.630750+00:00)

## What it is
A curated index of research papers and code for Graph Neural Network based recommender systems, accompanying an ACM TORS survey. It organizes algorithms by recommendation stages, scenarios, and objectives.

## Use cases
- find papers on GNN-based recommender systems
- survey graph neural networks for recommendation
- find implementations of NGCF or PinSage
- research sequential or social recommendation with GNNs
- get started with graph representation learning for recommendations

## When to choose
- you need a literature map of GNN recommendation research
- you want links to papers and code for specific GNN recommenders
- you are writing a survey or literature review on the topic

## When to avoid
- you need a ready-to-run recommendation library
- you want production recommender system code
- you need non-GNN recommendation algorithms

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, search-engine, developer-tools
- domain: machine-learning, deep-learning, data-science, tutorials, awesome-lists
- platform: python
- tags: gnn, recommender-systems, survey, graph-neural-networks, paper-index, collaborative-filtering

## Member repositories
- tsinghua-fib-lab/GNN-Recommender-Systems (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:29.630750+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:17.558441+00:00, confidence not recorded.
  - readme: https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems (fetched 2026-08-28T04:03:29.630750+00:00, sha 32d15721a5c2)
- Data as of 2026-08-30T08:39:29.467469+00:00.