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Information Systems Frontiers

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29-08-2017

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

Authors: Fedelucio Narducci, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro

Published in: Information Systems Frontiers | Issue 6/2018

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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.

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Title: TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning
Authors: Fedelucio Narducci
Cataldo Musto
Marco de Gemmis
Pasquale Lops
Giovanni Semeraro
Publication date: 29-08-2017
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 6/2018
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-017-9780-0

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