Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides

Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by

program genres

(documentary, sports, …) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program genre labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program genre. More specifically, we compare a statistical approach called

logistic regression

with an enhanced version of the commonly used vector space model, called

random indexing

, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on

explicit semantic analysis

for enriching the textual description associated to a TV show with additional features extracted from Wikipedia.