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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fedelucio Narducci & Cataldo Musto & Marco Gemmis & Pasquale Lops & Giovanni Semeraro, 2018. "TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning," Information Systems Frontiers, Springer, vol. 20(6), pages 1157-1171, December.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:6:d:10.1007_s10796-017-9780-0
    DOI: 10.1007/s10796-017-9780-0
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Ludovico Boratto & Salvatore Carta & Andreas Kaltenbrunner & Matteo Manca, 2018. "Guest Editorial: Behavioral-Data Mining in Information Systems and the Big Data Era," Information Systems Frontiers, Springer, vol. 20(6), pages 1153-1156, December.

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