Estimation of the click volume by large scale regression analysis

Abstract

How could one estimate the total number of clicks a new advertisement could potentially receive in the current market? This question, called the click volume estimation problem is investigated in this paper. This constitutes a new research direction for advertising engines. We propose a model of computing an estimation of the click volume. A key component of our solution is the application of linear regression to a large (but sparse) data set. We propose an iterative method in order to achieve a fast approximation of the solution. We prove that our algorithm always converges to optimal parameters of linear regression. To the best of our knowledge, it is the first time when linear regression is considered in such a large scale context. © Springer-Verlag Berlin Heidelberg 2007.