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Rank-mediated collaborative tagging recommendation service using video-tag relationship prediction

Author

Listed:
  • Wei-Feng Tung

    (Fu-Jen Catholic University)

  • Ting-Yu Lee

    (Fu-Jen Catholic University)

Abstract

A great many tags and videos are shared and created by a mass of distributors on Web 2.0 video sharing sites. This increasing user-generated content can further benefit service innovation of collaborative tagging. In order to enhance efficient video retrieval and online video marketing (OVM) application, this research proposes a rank-mediated collaborative tagging recommendation service that allows the distributors predicting the ranks of video retrieval from the shared video archive using vote-promotion algorithm (VPA). The system experiments evaluate the number of tags and videos between simple text retrieval and VPA. The user surveys verify the relevance, helpfulness, and satisfaction of the recommended tags. From the perspectives of service innovation, this research is to develop a systematic and quantified a video-tag relationship prediction and recommendation self-service that can provide an intelligent collaborative tagging service on video sharing sites.

Suggested Citation

  • Wei-Feng Tung & Ting-Yu Lee, 2013. "Rank-mediated collaborative tagging recommendation service using video-tag relationship prediction," Information Systems Frontiers, Springer, vol. 15(4), pages 627-635, September.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:4:d:10.1007_s10796-013-9436-7
    DOI: 10.1007/s10796-013-9436-7
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    References listed on IDEAS

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    1. Vargo, Stephen L. & Maglio, Paul P. & Akaka, Melissa Archpru, 2008. "On value and value co-creation: A service systems and service logic perspective," European Management Journal, Elsevier, vol. 26(3), pages 145-152, June.
    2. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
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    Cited by:

    1. Mukul Gupta & Pradeep Kumar & Bharat Bhasker, 2019. "DPRel: A Meta-Path Based Relevance Measure for Mining Heterogeneous Networks," Information Systems Frontiers, Springer, vol. 21(5), pages 979-995, October.
    2. Casey K. Fung & Patrick C. K. Hung, 2013. "Information and knowledge management in online rich presence services," Information Systems Frontiers, Springer, vol. 15(4), pages 521-523, September.

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