2 papers
cs.LG2020
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Hongjian Wang, Qi Li, Lanbo Zhang +4
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads. In this paper, we study how to effici…
stat.ML2019
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
Sofia Ira Ktena, Alykhan Tejani, Lucas Theis +5
One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad…