TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning

Fedelucio Narducci (), Cataldo Musto (), Marco Gemmis (), Pasquale Lops () and Giovanni Semeraro ()
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Fedelucio Narducci: University of Bari Aldo Moro
Cataldo Musto: University of Bari Aldo Moro
Marco Gemmis: University of Bari Aldo Moro
Pasquale Lops: University of Bari Aldo Moro
Giovanni Semeraro: University of Bari Aldo Moro

Information Systems Frontiers, 2018, vol. 20, issue 6, No 2, 1157-1171

Abstract: Abstract Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this 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 paper we introduce the concept of personal channel, on which Personalized EPGs are grounded, that provides users with potentially interesting programs and videos, by exploiting program genres (documentary, sports, …) and short textual descriptions of programs to find and categorize them. We investigate the problem of adopting appropriate algorithms for TV-program classification and retrieval, in the context of building personal channels, which is harder than a classical retrieval or classification task because of the short text available. The approach proposed to overcome this problem is the adoption of a new feature generation technique that enriches the textual program descriptions with additional features extracted from Wikipedia. Results of the experiments show that our approach actually improves the retrieval performance, while a limited positive effect is observed on classification accuracy.

Keywords: Recommender systems; Electronic program guides; Content-based filtering (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10796-017-9780-0

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