Behavioural Targeting in On-Line Advertising: An Empirical Study

Abstract

On-line behavioural targeting is a dynamically evolving area of web mining concerning the applications of data analysis of on-line users’ behaviour and machine learning in optimising web on-line advertising and constitutes a problem of high importance and complexity.

The paper reports on experimental work concerning testing various benchmark machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting.

Our final goal is to build a system which automatically learns and subsequently decides which on-line advertisements to present to a user visiting a web page, based on his previous recorded behaviour, in order to maximise the revenue of the ad-network and the web site hosting the ad, and, at the same time, to minimise the user’s annoyance caused by potentially inappropriate advertisements.

We present a general adaptive model which makes it possible to test various machine-learning algorithms in a plug-in mode and its implementation.

We also report our experimental work concerning comparison of the performance of various machine-learning algorithms and data preprocessing techniques on a real dataset.

The performance of our experiments is evaluated by some objective metrics such as the click-through rate (CTR) or related.

Our experimental results clearly indicate that the presented adaptive system can significantly increase the CTR metric by 40% for some settings.

All the experiments are performed on a real industrial dataset concerning on-line ads emitted in the Polish Web during a 1-month period in 2007.

Up to the authors’ best knowledge this is the first and largest evaluation made on this kind of real industrial data in Poland, at the time of writing.