shenweichen/DeepCTR-Torch

Health v2 · maintenance only

Adoption not part of the score

3450 stars · 735 forks observed · 2026-08-28

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

DeepCTR-Torch is a PyTorch library providing easy-to-use, modular, and extendable implementations of deep-learning-based CTR (click-through rate) prediction models such as DeepFM, xDeepFM, and Wide & Deep. It exposes a scikit-learn-like fit/predict API and reusable component layers for building custom models.

Use cases

• train a DeepFM model for click-through rate prediction
• build a custom CTR model in PyTorch from modular layers
• compare deep learning CTR models like xDeepFM and FiBiNET
• predict user ad click probability from multi-field categorical data
• prototype deep learning recommendation ranking models

When to choose

• you need ready-made implementations of many published CTR models in PyTorch
• you want a simple fit/predict workflow for CTR experiments
• you want modular layers to compose your own custom CTR architecture

When to avoid

• you need a full production recommender system rather than model implementations
• you prefer TensorFlow, where the original DeepCTR may fit better
• your task is not click-through-rate or user response prediction

Facets

library · maturity active

machine-learning deep-learning machine-learning deep-learning data-science python ctr click-through-rate-prediction recommender-systems pytorch deepfm xdeepfm torchrec

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