ISSN : 1796-203X
Volume : 1    Issue : 3    Date : June 2006

Efficient Formulations for 1-SVM and their Application to Recommendation Tasks
Yasutoshi Yajima and Tien-Fang Kuo
Page(s): 27-34
Full Text:
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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.

Index Terms
support vector machine, Laplacian matrix, graph kernel, quadratic programming problem,
collaborative filtering, recommender system