Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20788
Title: An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems
Authors: Luo, X
Zhou, M
Li, S 
Xia, Y
You, Z
Zhu, Q
Leung, H 
Keywords: Collaborative-filtering
Hessian-free Optimization
Incomplete Matrices
Latent Factor Model
Recommender Systems
Second-order Optimization
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial informatics, 2015, v. 11, no. 4, p. 946-956 How to cite?
Journal: IEEE transactions on industrial informatics 
Abstract: Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders. ? 2005-2012 IEEE.
URI: http://hdl.handle.net/10397/20788
ISSN: 1551-3203
EISSN: 1941-0050
DOI: 10.1109/TII.2015.2443723
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