# ZiyaoGeng/RecLearn

Recommender Learning with Tensorflow2.x

Repository: https://github.com/ZiyaoGeng/RecLearn
Canonical: https://ross.abutalabs.com/products/reclearn
Language: Python
License: MIT
License Family: permissive
Topics: recommender-system, python3, ctr-prediction, criteo, neural-network, factorization-machine, tensorflow2, deepfm, afm, xdeepfm, pnn, widedeep, nfm, ncf, dcn, deepcross, fm, ffm, matrix-factorization
Last push: 2022-04-29T06:10:37+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2348, "days_push": 1587, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1986, forks 490 (observed 2026-08-28T04:06:02.765413+00:00)

## What it is
RecLearn is a Python/TensorFlow2.x library implementing a wide collection of recommendation algorithms, covering both matching (Top-K recommendation) and ranking (CTR prediction) stages. It is aimed at students and beginners, with runnable examples on datasets like MovieLens and Amazon.

## Use cases
- implement recommender system models in tensorflow 2
- train CTR prediction models like DeepFM and xDeepFM
- learn matching and ranking recommendation algorithms
- run recommendation experiments on MovieLens or Amazon datasets
- study matrix factorization and factorization machine implementations
- prototype sequential recommendation models

## When to choose
- you want a beginner-friendly TensorFlow2 codebase for learning recommendation algorithms
- you need reference implementations of classic CTR and matching models
- you want runnable examples on standard recommendation datasets

## When to avoid
- you need production-scale, high-performance recommendation serving
- you require PyTorch or non-TensorFlow frameworks
- you need actively maintained code with recent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, data-science, education
- platform: python
- tags: recommender-system, tensorflow2, ctr-prediction, collaborative-filtering, factorization-machine, deepfm, matrix-factorization, top-k-recommendation, learning-resource

## Member repositories
- ZiyaoGeng/RecLearn (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:02.765413+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 2026-08-30T03:03:02.243718+00:00, confidence not recorded.
  - readme: https://github.com/ZiyaoGeng/RecLearn (fetched 2026-08-28T04:06:02.765413+00:00, sha d410e2578461)
  - registry_pypi: https://pypi.org/pypi/reclearn/json (fetched 2026-08-29T10:42:50.713811+00:00, sha 0e1eda821421)
- Data as of 2026-08-30T08:39:29.467469+00:00.