Interactive Television Personalization

Abstract

The personalized Electronic Program Guide (pEPG) has been touted as a possible solution to the information overload problem faced by Digital TV (DTV) users. It leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile viewing guides that fit their individual preferences. In this chapter, we focus on the recommendation technology used by existing pEPG’s and argue that certain important shortcomings (related to profile sparsity and recommendation diversity) exist that impact the future success of pEPG’s. We describe how data mining approaches can be used to alleviate many of these problems and present results of a comprehensive evaluation of such approaches.