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. © 2012 Springer-Verlag.

Musto, C., Narducci, F., Lops, P., Semeraro, G., De Gemmis, M., Barbieri, M., et al. (2012). Enhanced semantic TV-show representation for personalized electronic program guides. In J. Masthoff, B. Mobasher, M.C. Desmarais, R. Nkambou (a cura di), User Modeling, Adaptation, and Personalization (pp. 188-199). Springer [10.1007/978-3-642-31454-4_16].

Enhanced semantic TV-show representation for personalized electronic program guides

NARDUCCI, FEDELUCIO
Secondo
;
2012

Abstract

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. © 2012 Springer-Verlag.
Capitolo o saggio
Explicit Semantic Analysis; Logistic Regression; Personalized Electronic Program Guides; Random Indexing; Vector Space Model; Computer Science (all); Theoretical Computer Science
English
User Modeling, Adaptation, and Personalization
Masthoff, J;Mobasher, B; Desmarais,MC;Nkambou, R;
2012
9783642314537
7379
Springer
188
199
Musto, C., Narducci, F., Lops, P., Semeraro, G., De Gemmis, M., Barbieri, M., et al. (2012). Enhanced semantic TV-show representation for personalized electronic program guides. In J. Masthoff, B. Mobasher, M.C. Desmarais, R. Nkambou (a cura di), User Modeling, Adaptation, and Personalization (pp. 188-199). Springer [10.1007/978-3-642-31454-4_16].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/78534
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