# cheungdaven/DeepRec

An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

Repository: https://github.com/cheungdaven/DeepRec
Canonical: https://ross.abutalabs.com/products/cheungdaven-deeprec
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: recommender-systems, deep-learning, tensorflow, recommendation, recommendation-system, neural-network, python, collaborative-filtering, rating-prediction, top-n-recommendations, factorization-machine, matrix-factorization
Last push: 2022-06-01T19:41:17+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": 3035, "days_push": 1554, "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 1160, forks 290 (observed 2026-08-28T04:03:48.998960+00:00)

## What it is
DeepRec is an open-source Python/TensorFlow toolkit implementing deep learning based recommendation models for rating prediction, top-N item ranking, and sequential recommendation. It provides modular, extensible implementations of research models like AutoRec, NeuMF, NFM, CML, LRML, and Caser to help researchers reproduce state-of-the-art methods.

## Use cases
- reproduce deep learning recommendation models from papers
- build a rating prediction model
- implement top-N item ranking recommendations
- run sequential recommendation experiments
- compare collaborative filtering baselines
- learn how neural recommendation models are implemented in TensorFlow

## When to choose
- you need reference implementations of published deep recommendation models
- you are doing academic research on recommender systems
- you want an extensible framework to add new recommendation models
- you work with TensorFlow 1.x and Python 3

## When to avoid
- you need production-ready recommendation serving at scale
- you require TensorFlow 2.x support
- you need actively maintained code with frequent updates
- you want a turnkey recommender with minimal coding

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, data-science
- platform: python, cross-platform
- tags: recommender-systems, tensorflow, collaborative-filtering, matrix-factorization, factorization-machine, rating-prediction, top-n-recommendation, sequential-recommendation, research-toolkit, algorithms

## Member repositories
- cheungdaven/DeepRec (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:48.998960+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 2026-08-30T06:31:47.896331+00:00, confidence not recorded.
  - readme: https://github.com/cheungdaven/DeepRec (fetched 2026-08-28T04:03:48.998960+00:00, sha 2ee0b471f711)
- Data as of 2026-08-30T08:39:29.467469+00:00.