Ontological analysis of web surf history to maximize the click-through probability of web advertisements

Due to an enormous influx of capital over the past decade, the online advertising industry has become extremely robust and competitive. The difference between success and failure in such a competitive market often rests in the ability to deliver advertisements that are closely in line with a user's interests. In this work, we propose and test a new online advertisement targeting technique which adapts and utilizes several powerful and well tested information retrieval and lexical techniques to develop an estimate of a user's affinity for particular products and services based on an analysis of a user's web surfing behavior. This new online ad targeting technique performs extremely well in our empirical tests.