IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v20y2018i6d10.1007_s10796-017-9780-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-017-9780-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-017-9780-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    2. Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    3. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    4. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    5. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    6. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    7. Klaus Gugler & Florian Szücs & Ulrich Wohak, 2023. "Start-up Acquisitions, Venture Capital and Innovation: A Comparative Study of Google, Apple, Facebook, Amazon and Microsoft," Department of Economics Working Papers wuwp340, Vienna University of Economics and Business, Department of Economics.
    8. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    9. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    10. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    11. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    12. Gissler, Stefan & Oldfather, Jeremy & Ruffino, Doriana, 2016. "Lending on hold: Regulatory uncertainty and bank lending standards," Journal of Monetary Economics, Elsevier, vol. 81(C), pages 89-101.
    13. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    14. Alina Evstigneeva & Mark Sidorovskiy, 2021. "Assessment of Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 3-33, September.
    15. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    16. Hei-Chia Wang & Tzu-Ting Hsu & Yunita Sari, 2019. "Personal research idea recommendation using research trends and a hierarchical topic model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1385-1406, December.
    17. Hiroaki Sugino & Tatsuya Sekiguchi & Yuuki Terada & Naoki Hayashi, 2023. "“Future Compass”, a Tool That Allows Us to See the Right Horizon—Integration of Topic Modeling and Multiple-Factor Analysis," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    18. David A. Broniatowski, 2018. "Building the tower without climbing it: Progress in engineering systems," Systems Engineering, John Wiley & Sons, vol. 21(3), pages 259-281, May.
    19. Marcin Chlebus & Maciej Stefan Świtała, 2020. "So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison," Working Papers 2020-16, Faculty of Economic Sciences, University of Warsaw.
    20. Roman Jurowetzki, 2015. "Unpacking Big Systems - Natural Language Processing meets Network Analysis. A Study of Smart Grid Development in Denmark," SPRU Working Paper Series 2015-15, SPRU - Science Policy Research Unit, University of Sussex Business School.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:20:y:2018:i:6:d:10.1007_s10796-017-9780-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.