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Automatic generation of mashups for personalized commerce in digital TV by semantic reasoning

Abstract:

The evolution of information technologies is consolidating recommender systems as essential tools in e-commerce. To date, these systems have focused on discovering the items that best match the preferences, interests and needs of individual users, to end up listing those items by decreasing relevance in some menus. In this paper, we propose extending the current scope of recommender systems to better support trading activities, by automatically generating interactive applications that provide the users with personalized commercial functionalities related to the selected items. We explore this idea in the context of Digital TV advertising, with a system that brings together semantic reasoning techniques and new architectural solutions for web services and mashups. © 2009 Springer Berlin Heidelberg.