Online advertising has seen exponential growth since its in ception over 15 years ago, resulting in 2013 for the first time ever to exceed broadcast television advertising revenues. This success has arisen largely from the transformation of the advertising industry from a low tech, human intensive way of doing work (that were common place for much of the 20th century and the early days of online advertising) to highly optimised, mathematical, machine learning-centric processes that form the backbone of many current online advertising systems. Online advertising is a complex problem, especially from machine learn ing point of view. It contains multiple parties (advertisers, users, pub lishers and ad-networks), which interact with each other harmoniously but exhibit a conflict of interest when it comes to risk and revenue ob jectives. It is highly dynamic in terms of the rapid change of user infor mation needs and the frequent modifications of ads campaigns. It is very large scale, with billions of keywords, tens of millions of ads, billions of users, millions of advertisers where events such as clicks and actions can be extremely rare. The goal of this paper is to overview the state of the art in online adver tising and to propose a linear programming model for scheduling online ads. We tested the proposed system on the web site Time.mk and in this paper we present the results and improvements of the click-through rates (CTR) of the proposed approach.
online advertising, linear programming, machine learning