One-Class Support Vector Machines for Recommendation Tasks

  • Yasutoshi Yajima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


The present paper proposes new approaches for recommendation tasks based on one-class support vector machines (1-SVMs) with graph kernels generated from a Laplacian matrix. We introduce new formulations for the 1-SVM that can manipulate graph kernels quite efficiently. We demonstrate that the proposed formulations fully utilize the sparse structure of the Laplacian matrix, which enables the proposed approaches to be applied to recommendation tasks having a large number of customers and products in practical computational times. Results of various numerical experiments demonstrating the high performance of the proposed approaches are presented.


Recommender System Preference Score Kernel Matrix Collaborative Filter Laplacian Matrix 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Belkin, M., Niyogi, P.: Semi-supervised learning on Riemannian manifolds. Machine Learning 56, 209–239 (2004)CrossRefMATHGoogle Scholar
  2. 2.
    Chung, F.R.: Spectral Graph Theory. American Mathematical Society (1997)Google Scholar
  3. 3.
    Fouss, F., Pirotte, A., Saerens, M.: A novel way of computing dissimilarities between nodes of a graph, with application to collaborative filtering. In: ECML/SAWM, pp. 26–37 (2004)Google Scholar
  4. 4.
    Ito, T., Shimbo, M., Kudo, T., Matsumoto, Y.: Application of kernels to link analysis. In: KDD 2005, pp. 586–592 (2005)Google Scholar
  5. 5.
    Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  6. 6.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: EC 2000: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167 (2000)Google Scholar
  7. 7.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: ACM CHI 1995, pp. 210–217 (1995)Google Scholar
  9. 9.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  10. 10.
    Smola, A., Kondor, I.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS, vol. 2777, pp. 144–158. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Szummer, M., Jaakkola, T.: Partially labeled classification with Markov random walks. Advances in Neural Information Processing Systems 14, 945–952 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasutoshi Yajima
    • 1
  1. 1.Department of Industrial Engineering and ManagementTokyo Institute of TechnologyTokyoJapan

Personalised recommendations