# NVIDIA/DeepRecommender

Deep learning for recommender systems

Repository: https://github.com/NVIDIA/DeepRecommender
Canonical: https://ross.abutalabs.com/products/deeprecommender
Language: Python
License: MIT
License Family: permissive
Topics: deep-autoencoders, collaborative-filtering, deep-learning, gpu, recommendation-engine
Archived: true
Last push: 2021-05-17T23:38:49+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3281, "days_push": 1934, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1701, forks 341 (observed 2026-08-28T04:05:24.312728+00:00)

## What it is
A research library implementing deep autoencoders for collaborative filtering in recommender systems, based on the paper 'Training Deep AutoEncoders for Collaborative Filtering'. It is built with PyTorch and optimized for NVIDIA GPUs with CUDA support.

## Use cases
- train deep autoencoder models for collaborative filtering
- build recommendation engines using deep learning
- reproduce results from the deep autoencoder collaborative filtering paper
- train recommender models on the Netflix Prize dataset
- experiment with mixed precision training on Tensor Cores for recommender models

## When to choose
- you need GPU-accelerated deep autoencoder models for collaborative filtering
- you want to reproduce or extend the research from the associated paper
- you are working with large rating datasets like Netflix Prize
- you want a PyTorch-based research starting point for recommender systems

## When to avoid
- you need a production-ready, actively maintained recommendation framework
- you need a general-purpose recommendation library with many algorithm options
- you don't have access to an NVIDIA GPU
- you need long-term support or frequent updates

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python
- tags: recommender-systems, collaborative-filtering, autoencoders, pytorch, research, recommendation-systems, gpu, linux

## Member repositories
- NVIDIA/DeepRecommender (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.312728+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 2026-08-30T03:37:42.038400+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/DeepRecommender (fetched 2026-08-28T04:05:24.312728+00:00, sha 215f7eecadbe)
- Data as of 2026-08-30T08:39:29.467469+00:00.