Logistic Regression Setup for RTB CTR Estimation

In this paper we investigate one of the most interesting problems of Big Data user feedback prediction which is the Real-Time Bidding Click-Through Rate estimation. We evaluate experimentally the impact of the widely-referenced methods for optimization of the logistic regression - the state-of-the art Real-Time Bidding optimization method - on the quality of CTR estimation. From the perspective of this impact, we evaluate different configurations of widely-referenced regularization techniques and compare them with a simple technique of the feature generalization. On the basis of the results of the extensive experimentation, we show that in the context of the application scenario investigated herein, an optimization of the stochastic gradient descent algorithm configuration may be successfully accompanied, or even replaced, by a simple feature generalization.