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A Web Recommendation Technique Based on Probabilistic Latent Semantic Analysis

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Web Information Systems Engineering – WISE 2005 (WISE 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3806))

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Abstract

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of clickstream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

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Author information

Authors and Affiliations

  1. School of Computer Science and Mathematics, Victoria University, PO Box 14428, VIC, 8001, Australia

    Guandong Xu & Yanchun Zhang

  2. School of Information Technology & Electrical Engineering, University of Queensland, Brisbane, QLD, 4072, Australia

    Xiaofang Zhou

Authors
  1. Guandong Xu
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  2. Yanchun Zhang
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  3. Xiaofang Zhou
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Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, TX,  

    Anne H. H. Ngu

  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan

    Masaru Kitsuregawa

  3. University of Vienna, Vienna, Austria

    Erich J. Neuhold

  4. IBM Research Division, Thomas J. Watson Research Center, P.O. Box 218, 10598, New York, Yorktown Heights, USA

    Jen-Yao Chung

  5. School of Computer Science and Engineering, University of New South Wales, NSW 2052, Sydney, Australia

    Quan Z. Sheng

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© 2005 Springer-Verlag Berlin Heidelberg

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Xu, G., Zhang, Y., Zhou, X. (2005). A Web Recommendation Technique Based on Probabilistic Latent Semantic Analysis. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, JY., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2005. WISE 2005. Lecture Notes in Computer Science, vol 3806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581062_2

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  • DOI: https://doi.org/10.1007/11581062_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30017-5

  • Online ISBN: 978-3-540-32286-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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