Advertisement

Discovery of Relationships between Interests from Bulletin Board System by Dissimilarity Reconstruction

  • Kou Zhongbao
  • Ban Tao
  • Zhang Changshui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

In this paper, we propose a new method to analyze people’s interests by simulating the gradually changing and transferring mechanism among interests. Different boards in Bulletin Board System (BBS) which focus on various topics serve as representation of people’s interests. A technique named Dissimilarity Reconstruction (DSR) is put forward to discover relationships between the interests. DSR tries to grasp the intrinsic structure of the data set by the following steps. First, Vector Space Model (VSM) representations of the interests are obtained by taking users in BBS as terms and the numbers of messages they post as weights. Second, dissimilarities are calculated from the interest vectors. Finally, the nonlinear technique Isomap is engaged to map the interests into the intrinsic dimensional space of the data set where Euclidean distance between two interests well represents their relationship.

Keywords

Dimensionality Reduction Geodesic Distance Vector Space Model Neighborhood Graph Geodesic Path 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kou, Z.B., Zhang, C.S.: Reply Networks on a Bulletin Board System. Phys. Rev. E 67, 36117 (2003)Google Scholar
  2. 2.
    Balabanovic, M.: Learning to Surf: Multiagent Systems for AdaptiveWeb Page Recommendation. Ph.D. dissertation, Stanford University: Department Of Computer Science (1998)Google Scholar
  3. 3.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. Fifteenth International Conference on Machine Learning, Madison, WI, pp. 46–53 (1998)Google Scholar
  4. 4.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar
  5. 5.
    Duda, R.O., et al.: Pattern Classification. John Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  6. 6.
    Tenenbaum, J.B., et al.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  7. 7.
    Steyvers, M.: Multidimensional Scaling. In: Encyclopedia of Cognitive Science, Macmillan, London (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kou Zhongbao
    • 1
  • Ban Tao
    • 1
  • Zhang Changshui
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of AutomationTsinghua UniversityBeijingP.R.China

Personalised recommendations