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Context-Awareness and Viewer Behavior Prediction in Social-TV Recommender Systems: Survey and Challenges

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New Trends in Databases and Information Systems (ADBIS 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 539))

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  • East European Conference on Advances in Databases and Information Systems

Abstract

This paper surveys the landscape of actual personalized TV recommender systems, and introduces challenges on context-awareness and viewer behavior prediction applied to social TV-recommender systems. Real data related to the viewers behaviors and the social context have been picked up in real-time through a social TV platform. We highlighted the future benefits of analyzing viewer behavior and exploiting the social influence on viewers’s preferences to improve recommendation in respect with TV contents’ change.

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Author information

Authors and Affiliations

  1. LARODEC, ISG, University of Tunis, Le Bardo, Tunisia

    Mariem Bambia

  2. IRIT, University of Paul Sabatier, Toulouse, France

    Mariem Bambia & Mohand Boughanem

  3. LARODEC, IHEC, University of Carthage, Carthage Presidency, Tunis, Tunisia

    Rim Faiz

Authors
  1. Mariem Bambia
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  2. Rim Faiz
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  3. Mohand Boughanem
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Corresponding author

Correspondence to Mariem Bambia .

Editor information

Editors and Affiliations

  1. Poznan University of Technology, Poznan, Poland

    Tadeusz Morzy

  2. INRIA, Montpellier, France

    Patrick Valduriez

  3. National Engineering School for Mechanics and Aerotechnics, Poitiers, France

    Ladjel Bellatreche

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Bambia, M., Faiz, R., Boughanem, M. (2015). Context-Awareness and Viewer Behavior Prediction in Social-TV Recommender Systems: Survey and Challenges. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-23201-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23200-3

  • Online ISBN: 978-3-319-23201-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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