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Toward Building a Content-Based Video Recommendation System Based on Low-Level Features

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E-Commerce and Web Technologies (EC-Web 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 239))

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Abstract

One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, everyday, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations.

In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features.

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Acknowledgments

This work is supported by Telecom Italia S.p.A., Open Innovation Department, Joint Open Lab S-Cube, Milan.

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Authors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana & Paolo Cremonesi

Authors
  1. Yashar Deldjoo
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  2. Mehdi Elahi
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  3. Massimo Quadrana
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  4. Paolo Cremonesi
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Corresponding author

Correspondence to Yashar Deldjoo .

Editor information

Editors and Affiliations

  1. Institut für Informatik, B6.26, University of Mannheim Institut für Informatik, B6.26, Mannheim, Germany

    Heiner Stuckenschmidt

  2. Dep. of Computer Science, TU Dortmund Dep. of Computer Science, Dortmund, Germany

    Dietmar Jannach

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Deldjoo, Y., Elahi, M., Quadrana, M., Cremonesi, P. (2015). Toward Building a Content-Based Video Recommendation System Based on Low-Level Features. In: Stuckenschmidt, H., Jannach, D. (eds) E-Commerce and Web Technologies. EC-Web 2015. Lecture Notes in Business Information Processing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-27729-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-27729-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27728-8

  • Online ISBN: 978-3-319-27729-5

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