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Investigation of the Effectiveness of Tag-Based Contextual Collaborative Filtering in Website Recommendation

  • Reyn Nakamoto
  • Shinsuke Nakajima
  • Jun Miyazaki
  • Shunsuke Uemura
  • Hirokazu Kato
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

As the Internet continues to mature and become more accessible to the common user, the amount of available information increases exponentially. Accordingly, finding useful and relevant information is becoming progressively difficult. Moreover, a lot of the information available—blogs, various types of reviews, and so forth— is highly subjective and thus, hard to evaluate purely through machine algorithms. Being subjective in nature, one person may absolutely love something while the next may loathe the same—no single authority exists. It is in these cases where people— more so than the current ability of machine algorithms—are greatly effective in evaluating and filtering this information.

Keywords

Collaborative Filter Score Prediction Similar User Recommendation Method Computer Support Cooperative Work 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    del.icio.us, http://del.icio.us/
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    S. Golder and B. Huberman. The structure of collaborative tagging systems. http://www.hpl.hp.com/research/idl/papers/tags/tags.pdf
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    Nakamoto R, Nakajima S, Miyazaki J, Uemura S (2007) Contextual collaborative filtering based on tagging information. In Proceedings of The International MultiConference of Engineers and Computer Scientists pages 964–969Google Scholar
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    Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J (1994) GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of ACM Conference on Computer Supported Cooperative Work pages 175–186, Chapel Hill, North Carolina. ACM. http://citeseer.ist.psu.edu/resnick94grouplens.html

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Reyn Nakamoto
    • 1
  • Shinsuke Nakajima
    • 1
  • Jun Miyazaki
    • 1
  • Shunsuke Uemura
    • 2
  • Hirokazu Kato
    • 1
  1. 1.Graduate School of Information ScienceNara Institute of Science and TechnologyJapan
  2. 2.Faculty of InformaticsNara Sangyo UniversityJapan

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