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Personalized Recommendation Based on Behavior Sequence Similarity Measures

  • Conference paper
Behavior and Social Computing (BSIC 2013, BSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8178))

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Abstract

Personalized recommendation is attracting more and more attentions nowadays. There are many kinds of algorithms for making predictions for the target users, and among them Collaborative Filtering (CF) is widely adopted. In some domains, a user’s behavior sequences reflect his/her preferences over items so that users who have similar behavior sequences may indicate they have similar preference models. Based on this fact, we discuss how to improve the collaborative filtering algorithm by using user behavior sequence similarity. We proposed a new Behavior Sequence Similarity Measurement (BSSM) approach. Then, different ways to combine BSSM with CF algorithm are presented. Experiments on two real test data sets prove that more precise and stable recommendation performances can be achieved.

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Author information

Authors and Affiliations

  1. Shanghai Jiaotong University, China

    Yuqi Zhang & Jian Cao

Authors
  1. Yuqi Zhang
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  2. Jian Cao
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Editor information

Editors and Affiliations

  1. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, NSW 2008, Sydney, Australia

    Longbing Cao

  2. Institute of Scientific and Industrial Research, Osaka University, Japan

    Hiroshi Motoda

  3. Department of Computer Science, University of Minnesota, USA

    Jaideep Srivastava

  4. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim

  5. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Rm 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

  7. Leibniz Universität Hannover, Germany

    Wolfgang Nejdl

  8. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

  9. Deakin University, Burwood, VIC, Australia

    Gang Li

  10. Shanghai Jiao Tong University, 200240, Shanghai, China

    Ya Zhang

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© 2013 Springer International Publishing Switzerland

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Zhang, Y., Cao, J. (2013). Personalized Recommendation Based on Behavior Sequence Similarity Measures. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-04048-6_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04047-9

  • Online ISBN: 978-3-319-04048-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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