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Personalized News Video Recommendation Via Interactive Exploration

  • Conference paper
Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

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Abstract

In this paper, we have developed an interactive approach to enable personalized news video recommendation. First, multi-modal information channels (audio, video and closed captions) are seamlessly integrated and synchronized to achieve more reliable news topic detection, and the contextual relationships between the news topics are extracted automatically. Second, topic network and hyperbolic visualization are seamlessly integrated to achieve interactive navigation and exploration of large-scale collections of news videos at the topic level, so that users can have a good global overview of large-scale collections of news videos at the first glance. In such interactive topic network navigation and exploration process, the user’s personal background knowledge can be taken into consideration for obtaining the news topics of interest interactively, building up their mental models of news needs precisely and formulating their searches easily by selecting the visible news topics on the screen directly. Our system can further recommend the relevant web news, the new search directions, and the most relevant news videos according to their importance and representativeness scores. Our experiments on large-scale collections of news videos have provided very positive results.

This paper is supported by Shanghai Pujiang Program under 08PJ1404600, Science and Technology Commission of Shanghai Municipality under 07dz5997, National Science Foundation of China under 60496325, 60803077 and National Hi-tech R&D Program of China under 2006AA010111.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, UNC, Charlotte, USA

    Jianping Fan

  2. Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China

    Hangzai Luo & Aoying Zhou

  3. Institute of Computer Science, University of Konstanz, Konstamz, Germany

    Daniel A. Keim

Authors
  1. Jianping Fan
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  2. Hangzai Luo
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  3. Aoying Zhou
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  4. Daniel A. Keim
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Fan, J., Luo, H., Zhou, A., Keim, D.A. (2008). Personalized News Video Recommendation Via Interactive Exploration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_37

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  • DOI: https://doi.org/10.1007/978-3-540-89646-3_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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