# DeepGraphLearning/RecommenderSystems

Repository: https://github.com/DeepGraphLearning/RecommenderSystems
Canonical: https://ross.abutalabs.com/products/recommendersystems
Language: Python
License: MIT
License Family: permissive
Last push: 2020-04-10T09:22:29+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": 2779, "days_push": 2336, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1154, forks 276 (observed 2026-08-28T04:03:47.594215+00:00)

## What it is
A Python research library collecting the authors' recommender systems code, covering sequential, feature-based (CTR), and social recommendation models. It also provides a paper reading list and benchmark datasets, including a large Douban dataset.

## Use cases
- build sequential recommendation models with RNNs or transformers
- run CTR prediction experiments with feature interaction models
- experiment with social/session-based recommendation using graph neural networks
- find benchmark datasets for recommendation research
- get a reading list of recommender systems papers

## When to choose
- you are doing academic research on recommendation algorithms
- you need reference implementations of sequential or social recommenders
- you want benchmark datasets like Douban for evaluation

## When to avoid
- you need a production-ready recommendation engine for an application
- you want a maintained library with active releases (last release 2020)
- you need plug-and-play recommenders rather than research code

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, benchmarking
- domain: machine-learning, data-science, analytics
- platform: python
- tags: recommender-systems, sequential-recommendation, ctr-prediction, social-recommendation, research-code, datasets, reading-list

## Member repositories
- DeepGraphLearning/RecommenderSystems (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:47.594215+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 2026-08-30T06:32:15.238777+00:00, confidence not recorded.
  - readme: https://github.com/DeepGraphLearning/RecommenderSystems (fetched 2026-08-28T04:03:47.594215+00:00, sha 2c841f47ebc8)
- Data as of 2026-08-30T08:39:29.467469+00:00.