GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Application of kernels to link analysis
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Estimating the Support of a High-Dimensional Distribution
Neural Computation
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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.