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Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads.
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.
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