Distributed Technologies for Personalized Advertisement Delivery

This chapter provides an overview on personalized advertisement delivery paradigms on the web with a focus on the recommendation of advertisements expressed in or accompanied by text. Different methods of online targeted advertising will be examined, while justifying the need for channeling the appropriate ads to the corresponding users. The aim of the work presented here is to illustrate how the semantic representation of ads and user preferences can achieve optimal and unobtrusive ad delivery. We propose a set of distributed technologies that efficiently handles the lack of textual data in ads by enriching ontological knowledge with statistical contextual data in order to classify ads and generic content under a uniform, machine-understandable vocabulary. This classification is used to construct lightweight semantic user profiles, matched with semantic ad descriptions via fuzzy semantic reasoning. A real world user study, as well as an evaluative exploration of framework alternatives validate the system’s effectiveness to produce high quality ad recommendations.

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