ChenglongChen/tensorflow-DeepFM

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2066 stars · 802 forks observed · 2026-08-28

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

A TensorFlow implementation of the DeepFM model combining factorization machines and deep neural networks for click-through-rate (CTR) prediction. It supports classification and regression tasks, configurable FM/DNN components, early stopping, and refitting.

Use cases

• predict click-through rates for online ads
• train a DeepFM model in TensorFlow
• build a CTR prediction model with factorization machines
• run deep learning based CTR models on sparse categorical features
• use FM or DNN components separately for recommendation tasks
• prepare Kaggle competition features for DeepFM

When to choose

• you need a proven DeepFM implementation for CTR or recommendation ranking tasks
• you work with sparse categorical feature fields and want FM plus deep learning in one model
• you want a simple, well-documented reference implementation for learning or benchmarking

When to avoid

• you need a maintained library compatible with modern TensorFlow 2.x
• you want production-grade feature engineering pipelines or serving infrastructure
• you need models beyond DeepFM/FM/DNN variants

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning data-science python deepfm factorization-machine ctr-prediction recommendation tensorflow click-through-rate

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