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Designing a Hybrid Recommendation System for TV Content

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Intelligent Decision Technology Support in Practice

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 42))

Abstract

In the area of knowledge-based information systems, TV recommendation systems have attracted researchers’ attention with the development of Smart TV and expansion of TV content . Traditional TV recommendation systems, which are based on individual’s viewing activity and only recommending TV programs, have been unable to meet the requirements of Smart TV. Hence, this chapter proposes a hybrid recommendation system, which not only uses the information of single user’s activity, but also takes into account other users’ viewing habits and related information from the Internet, for different content such as TV programs, movies, and music. The proposed recommendation system integrates Content Analysis Component, User Analysis Component and Preference Learning Component. Moreover, this chapter also discusses several important design issues, such as diversity, novelty, explanation and group recommendations, which should be considered in designing/building a TV recommendation system. The proposed framework could be used to help designers and developers to design a TV recommendation system engine for Smart TV.

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Notes

  1. 1.

    http://www.imdb.com/.

  2. 2.

    http://movies.yahoo.com/.

  3. 3.

    http://www.angieslist.com/.

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Authors and Affiliations

  1. Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan

    Na Chang, Mhd Irvan & Takao Terano

Authors
  1. Na Chang
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  2. Mhd Irvan
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  3. Takao Terano
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Corresponding author

Correspondence to Na Chang .

Editor information

Editors and Affiliations

  1. School of Engineering, KES Centre, University of South Australia , Adelaide, South Australia, Australia

    Jeffrey W. Tweedale

  2. FCT Campus, UNINOVA, Caparica, Portugal

    Rui Neves-Silva

  3. School of Engineering, University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  4. Sellinger School of Business and Management, Loyola University Maryland, Baltimore, Maryland, USA

    Gloria Phillips-Wren

  5. Graduate School of IPS, Waseda University, Fukuoka, Japan

    Junzo Watada

  6. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Chang, N., Irvan, M., Terano, T. (2016). Designing a Hybrid Recommendation System for TV Content. In: Tweedale, J., Neves-Silva, R., Jain, L., Phillips-Wren, G., Watada, J., Howlett, R. (eds) Intelligent Decision Technology Support in Practice. Smart Innovation, Systems and Technologies, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-21209-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-21209-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21208-1

  • Online ISBN: 978-3-319-21209-8

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