rixwew/pytorch-fm

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1080 stars · 229 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch library implementing a wide collection of factorization machine models (FM, FFM, DeepFM, xDeepFM, AutoInt, NFM, DCN, and more) for click-through rate prediction and recommendation. It also bundles loaders for common benchmark datasets like MovieLens, Criteo, and Avazu.

Use cases

• train factorization machine models in pytorch
• run ctr prediction experiments with deepfm or xdeepfm
• benchmark feature interaction models on criteo and avazu datasets
• implement neural collaborative filtering
• compare recommender model auc on movielens
• learn how factorization machines are implemented

When to choose

• you need many factorization machine variants in one pytorch package
• you want reproducible baselines on standard CTR datasets
• you are researching or teaching feature interaction models

When to avoid

• you need a production recommender system with serving infrastructure
• you work outside PyTorch or need distributed large-scale training
• you need actively developed features beyond the existing model set

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning data-science python factorization-machines ctr-prediction recommender-systems pytorch deepfm collaborative-filtering feature-interaction recommendation

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markdown · JSON · MCP: product_card(name="rixwew/pytorch-fm")