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

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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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Notes

  1. http://www.watchmi.tv/en

  2. http://www.aprico.tv

  3. http://www.watchmi.tv/en/watchmi-search

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Acknowledgments

This work fulfils the research objectives of the PAC02L1_00061 project MAIVISTO “Massive Adaptive Internet VIdeo STreaming using the clOud” funded by the Italian Ministry of University and Research (MIUR). The authors are grateful to Mauro Barbieri, Jan H. M. Korst, Verus Pronk, Ramon Clout from Philips Research Eindhoven, who provided expertise that greatly supported our research. The authors wish to thank Philips Research Eindhoven and Axel Springer for providing access to the dataset used in the experiments.

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Authors and Affiliations

  1. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

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

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  1. Fedelucio Narducci
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  2. Cataldo Musto
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  3. Marco de Gemmis
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  4. Pasquale Lops
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  5. Giovanni Semeraro
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Correspondence to Fedelucio Narducci.

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Narducci, F., Musto, C., de Gemmis, M. et al. TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning. Inf Syst Front 20, 1157–1171 (2018). https://doi.org/10.1007/s10796-017-9780-0

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