miscellaneous-10
Click-Through Rate Prediction
3260 papers • 126 benchmarks • 313 datasets
Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked. ( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in click-through-rate-prediction-10
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Libraries
iUse these libraries to find click-through-rate-prediction-10 models and implementations
30 papers 444
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Most implemented papers
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng, L. Koc, Jeremiah Harmsen, T. Shaked, Tushar Chandra, H. Aradhye, Glen Anderson, G. Corrado, Wei Chai, M. Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah•Thu Jun 23 2016
Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow.
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