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Type: Conference paper
Title: Scalable maximum margin matrix factorization by active Riemannian subspace search
Author: Yan, Y.
Tan, M.
Tsang, I.
Yang, Y.
Zhang, C.
Shi, Q.
Citation: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 / vol.2015-January, pp.3988-3994
Publisher: AAAI Press
Issue Date: 2015
ISBN: 9781577357384
ISSN: 1045-0823
Conference Name: Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI) (25 Jul 2015 - 31 Jul 2015 : Buenos Aires, Argentina)
Statement of
Yan Yan, Mingkui Tan, Ivor Tsang, Yi Yang, Chengqi Zhang and Qinfeng Shi
Abstract: The user ratings in recommendation systems are usually in the form of ordinal discrete values. To give more accurate prediction of such rating data, maximum margin matrix factorization (M³F) was proposed. Existing M³F algorithms, however, either have massive computational cost or require expensive model selection procedures to determine the number of latent factors (i.e. the rank of the matrix to be recovered), making them less practical for large scale data sets. To address these two challenges, in this paper, we formulate M³F with a known number of latent factors as the Riemannian optimization problem on a fixed-rank matrix manifold and present a block-wise nonlinear Riemannian conjugate gradient method to solve it efficiently. We then apply a simple and efficient active subspace search scheme to automatically detect the number of latent factors. Empirical studies on both synthetic data sets and large real-world data sets demonstrate the superior efficiency and effectiveness of the proposed method.
Rights: AAAI Press ©2015
RMID: 0030041292
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Appears in Collections:Computer Science publications

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