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A Characterized Rating Recommend System

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

In recent years, due to the rapid growth of Internet usage, the problem of how to avoid inappropriate Internet contents accessing becomes more and more important. To solve the problem, a Collaborative Rating System [3, 4] based upon PICS protocol has been proposed. However, since the users usually would like to consult the opinions of the user group with similar rating tendency rather than the common opinions from the majority, it means the opinion of second majority with sufficient number of voters should also be considered. So does third majority, and so on. In order to provide a characterized rating service, a Characterized Rating Recommend System is designed to provide more precise and proper rating service for each user. Also, in this work, a questionnaire is designed to get users’ opinions, and some experimental results show that the system can provide acceptable rating service.

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References

  1. ICRA, Internet Content Rating Association, http://www.icra.org, 2000

  2. A.K. Jain and R.C. Dubes, Algorithms for clustering data, Prentice-Hall Inc., pp. 58–89, 1988.

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  3. Mon-Fong Jiang, Shian-Shyong Tseng, and Yao-Tsung Lin, “Collaborative Rating System for Web Page Labeling,” World Conference of the WWW and Internet, Honolulu, Hawaii, 1999

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  4. Yao-Tsung Lin, Shian-Shyong Tseng, and Mon-Fong Jiang, “Voting Based Collaborative Platform for Internet Content Selection”, The 4th Global Chinese Conference on Computers in Education Proceeding, 2000

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  5. PICS, Platform for Internet Content Selection, http://www.w3.org/PICS, 2000

  6. J.R. Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers, Inc., 1988

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  7. RSAC, Recreational Software Advisory Council, http://www.rsac.org, 1999

  8. W3C, World Wide Web Consortium, http://www.w3.org, 2000

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© 2001 Springer-Verlag Berlin Heidelberg

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Lin, YT., Tseng, SS. (2001). A Characterized Rating Recommend System. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_7

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  • DOI: https://doi.org/10.1007/3-540-45357-1_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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