Efficient formulations for 1-SVM and their application to recommendation tasks

Journal of Computers

Volumn 1, Issue 3, 2006, Pages 27-34

  Yajima, Yasutoshi ^a   Kuo, Tien Fang ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Collaborative filtering; Graph kernel; Laplacian matrix; Quadratic programming problem; Recommender system; Support vector machine

Indexed keywords

COLLABORATIVE FILTERING; LAPLACE TRANSFORMS; MATRIX ALGEBRA; QUADRATIC PROGRAMMING; RECOMMENDER SYSTEMS;

COMPUTATIONAL TIME; EFFICIENT FORMULATION; GRAPH KERNELS; LAPLACIAN MATRICES; NEW APPROACHES; NUMERICAL EXPERIMENTS; ONE-CLASS SUPPORT VECTOR MACHINE; QUADRATIC PROGRAMMING PROBLEMS;

SUPPORT VECTOR MACHINES;

EID: 70449597165     PISSN: 1796203X     EISSN: None     Source Type: Journal    
DOI: 10.4304/jcp.1.3.27-34     Document Type: Article

References (14)

1. P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews, " in Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work. Chapel Hill, North Carolina: ACM, 1994, pp. 175-186.
2. U. Shardanand and P. Maes, "Social information filtering: Algorithms for automating "word of mouth", " in Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, vol. 1, 1995, pp. 210-217.
3. F. Fouss, A. Pirotte, and M. Saerens, "A novel way of computing dissimilarities between nodes of a graph, with application to collaborative filtering, " in 15th European Conference on Machine Learning (ECML 2004); Proceedings of the Workshop on Statistical Approaches for Web Mining (SAWM), 2004, pp. 26-37.
4. M. Szummer and T. Jaakkola, "Partially labeled classification with Markov random walks, " in Advances in Neural Information Processing Systems, vol. 14, 2002, pp. 945-952.
5. A. Smola and I. Kondor, "Kernels and regularization on graphs, " in Proceedings of the Annual Conference on Computational Learning Theory, ser. Lecture Notes in Computer Science, B. Schölkopf and M. Warmuth, Eds. Springer, 2003.
6. M. Belkin and P. Niyogi, "Semi-supervised learning on Riemannian manifolds, " Machine Learning, vol. 56, pp. 209-239, 2004.
7. T. Ito, M. Shimbo, T. Kudo, and Y. Matsumoto, "Application of kernels to link analysis, " in KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. NewYork, NY, USA: ACM Press, 2005, pp. 586-592.
8. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a highdimensional distribution, " Neural Computation, vol. 13, pp. 1443-1471, 2001.
9. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press, 2004.
10. F. R. Chung, Spectral Graph Theory. American Mathematical Society, 1997.
11. X. Zhu, J. Lafferty, and Z. Ghahramani, "Semi-supervised learning: From Gaussian fields to Gaussian processes, " Carnegie Mellon University, Technical Report CMU-CS-03-175, 2003.
12. O. L. Mangasarian and M. V. Solodov, "Nonlinear complementarity as unconstrained and constrained minimization, " Math. Programming, vol. 62, no. 2, Ser. B, pp. 277-297, 1993.
13. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, "Learning with local and global consistency, " Advances in Neural Information Processing Systems, vol. 16, pp. 321-328, 2004.
14. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Analysis of recommendation algorithms for e-commerce, " in EC '00: Proceedings of the 2nd ACM conference on Electronic commerce. New York, NY, USA: ACM Press, 2000, pp. 158-167.