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International Conference on Active Media Technology

AMT 2012: Active Media Technology pp 206–214Cite as

An Adaptive Method for the Tag-Rating-Based Recommender System

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7669))

Abstract

In this paper, we propose an adaptive method for recommender system based on users’ preference to items represented by the ratings of users. This method defines a term-association matrix to describe the relation between tags and items properties. A gradient descent method is employed to compute the association matrix. The association matrix is then used to implement the two kinds of recommendation, namely, tag recommendation and items properties recommendation.

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Author information

Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, China

    Xi Yuan & Jia-jin Huang

Authors
  1. Xi Yuan
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  2. Jia-jin Huang
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Information Sciences, Hosei University, 3-7-2, Kajino-cho, 184-8584, Koganei-shi, Tokyo, Japan

    Runhe Huang

  2. Faculty of Computer Science, University of New Brunswick, Box 440, E3B 5A3, Fredicton, NB, Canada

    Ali A. Ghorbani

  3. Department of Informatics, Systems and Communication, University of Milano Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Gabriella Pasi

  4. Department of Administration Engineering, Keio University, 3-14-1 Hiyoshi, 223-8522, Kohoku-ku, Yokohama, Japan

    Takahira Yamaguchi

  5. University of Aizu, 102-C, Research Quadrangles, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City, Fukushima, Japan

    Neil Y. Yen

  6. Institute of Software, Chinese Academy of Sciences, no.4 Nan Sie Jie, Zhong Guan Cun, Hai Dian Qu, 100190, Beijing, China

    Beijing Jin

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© 2012 Springer-Verlag Berlin Heidelberg

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Yuan, X., Huang, Jj. (2012). An Adaptive Method for the Tag-Rating-Based Recommender System. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_21

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  • DOI: https://doi.org/10.1007/978-3-642-35236-2_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35235-5

  • Online ISBN: 978-3-642-35236-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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