Are you looking for an easy way to build deep-learning based click-through rate (CTR) models? Look no further! DeepCTR is here to make your life easier. With its convenient and modular design, DeepCTR provides a comprehensive set of tools for creating, testing, and deploying CTR models.
Understanding DeepCTR
DeepCTR is an easy-to-use, modular, and extendible package for deep-learning based CTR models. It offers a collection of core components and layers that you can utilize to build custom models. Whether you need a quick experiment using high-level APIs or require large-scale data training with distributed training capabilities, DeepCTR has got you covered.
The package is compatible with both TensorFlow 1.x and TensorFlow 2.x, making it adaptable to different versions of the framework. It provides a user-friendly interface similar to tf.keras.Model, enabling you to easily train and predict with any complex model using model.fit() and model.predict().
Models List
DeepCTR encompasses a wide range of CTR models. Here are some of the models available in DeepCTR:
- Convolutional Click Prediction Model
- Factorization-supported Neural Network
- Product-based Neural Network
- Wide & Deep
- DeepFM
- Piece-wise Linear Model
- Deep & Cross Network
- Attentional Factorization Machine
- Neural Factorization Machine
- xDeepFM
- Deep Interest Network
- AutoInt
- Deep Interest Evolution Network
- FwFM
- ONN
- FGCNN
- Deep Session Interest Network
- FiBiNET
- FLEN
- BST
- IFM
- DCN V2
- DIFM
- FEFM and DeepFEFM
- SharedBottom
- ESMM
- MMOE
- PLE
- EDCN
Each model has a corresponding citation, allowing you to dive deeper into the research behind it. You can find the full list of models and their respective papers in the DeepCTR repository.
Getting Started with DeepCTR
Ready to start building your own CTR models with DeepCTR? Head over to the Quick Start guide, which provides a step-by-step tutorial on how to use DeepCTR. The guide includes code examples and explanations to help you get up and running quickly.
If you prefer a Chinese introduction, there is also a Chinese Quick Start available.
Join the Discussion
Have questions or want to connect with other DeepCTR users? Check out the GitHub Discussions or the Wechat Discussions (scan the QR code available in the README) to join the conversation. Share your experiences, ask for help, or contribute your knowledge to the community.
Acknowledging Contributors
DeepCTR is a collaborative effort, and many talented individuals have contributed to its development. Some of the main contributors include Shen Weichen, Zan Shuxun, Harshit Pande, Lai Mincai, Li Zichao, and Tan Tingyi. Their expertise and hard work have made DeepCTR what it is today.
Conclusion
DeepCTR is a valuable tool for software engineers and solution architects who want to build deep-learning based CTR models. Its easy-to-use interface, compatibility with TensorFlow, and extensive list of models make it a powerful asset for anyone working in the field of CTR prediction.
So why wait? Dive into the world of DeepCTR and start building impressive CTR models today!
References
- DeepCTR Repository: https://github.com/shenweichen/deepctr
- DeepCTR Documentation: https://deepctr-doc.readthedocs.io/
- Related Project: DeepMatch Repository – https://github.com/shenweichen/DeepMatch
- Related Project: DeepCTR-Torch Repository – https://github.com/shenweichen/DeepCTR-Torch
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