Skip to main content
SpringerLink
Log in

An Improved Recommender Based on Hidden Markov Model

  • Conference paper
PRICAI 2012: Trends in Artificial Intelligence (PRICAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7458))

Included in the following conference series:

Abstract

In reality, users rarely dedicate excessive interest into only one topic over a long time. We propose a topic-based hidden Markov model to analyze temporal dynamics of users’ preference. Experiments show that given observations of a new entrant, the proposed model is able to recommend a specific user group he/she can be classified into and also can anticipate what topic he/she will be mostly interested in.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  2. Xu, G., Gu, Y., Zhang, Y., Yang, Z., Kitsuregawa, M.: TOAST: A Topic-Oriented Tag-Based Recommender System. In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds.) WISE 2011. LNCS, vol. 6997, pp. 158–171. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Daud, A.: Using Time Topic Modeling for Semantics-Based Dynamic Research Interest Finding. Knowledge Based Systems 26 (2010)

    Google Scholar 

  4. Hartigan, J.A., Wong, M.A.: A K-means Clustering Algorithm. Journal of the Royal Statistical Society 28(1), 100–108 (1979)

    MATH  Google Scholar 

  5. Goldberg, K., Roeder, T., Gupta, D., et al.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Logic and Intelligence, Faculty of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Jialing Li, Li Li, Yuheng Wu & Shangxiong Chen

Authors
  1. Jialing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shangxiong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Environment, Society and Design, Department of Applied Computing, Lincoln University, P.O. Box 84, 7647, Christchurch, New Zealand

    Patricia Anthony

  2. School of Information Science and Technology, University of Tokyo, 7-3-1, Hongo, 113-8656, Bunkyo-ku, Tokyo, Japan

    Mitsuru Ishizuka

  3. MIMOS Berhad, Knowledge Technology, Technology Park Malaysia,, 57000, Kuala Lumpur, Malaysia

    Dickson Lukose

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, J., Li, L., Wu, Y., Chen, S. (2012). An Improved Recommender Based on Hidden Markov Model. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_88

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32695-0_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation