shenweichen/DeepCTR

Health v2 · maintenance only

Adoption not part of the score

8050 stars · 2219 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepCTR is a Python library of easy-to-use, modular, and extendible deep-learning based CTR (click-through rate) prediction models built on TensorFlow 1.15 and 2.x. It provides tf.keras-like fit/predict interfaces plus a TensorFlow Estimator interface for large-scale distributed training, along with reusable layers for building custom models.

Use cases

• train a DeepFM model for click-through rate prediction
• build custom CTR models from reusable layers
• run distributed CTR training on large datasets with TFRecord and Estimator
• experiment with models like DIN, xDeepFM, AutoInt, and DIEN
• do multi-task learning with MMOE, ESMM, or PLE
• benchmark deep CTR models on the Criteo dataset

When to choose

• you need production-ready deep CTR models in TensorFlow with a familiar keras-style API
• you want many state-of-the-art CTR and multi-task models behind one consistent interface
• you need both quick experimentation and large-scale distributed training paths

When to avoid

• your stack is PyTorch (use DeepCTR-Torch instead)
• you need general recommendation/retrieval models rather than CTR prediction (consider DeepMatch)
• you want a framework-agnostic or non-TensorFlow solution

Facets

library · maturity stable

machine-learning deep-learning machine-learning deep-learning data-science python ctr click-through-rate recommendation tensorflow keras factorization-machines deepfm din multi-task-learning gpu

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