Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 11, Issue 4, Pages 946-956Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2015.2443723
Keywords
Collaborative filtering (CF); Hessian-free optimization; incomplete matrices; latent-factor (LF) model; recommender systems; second-order optimization
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Funding
- PRC Ministry of Science and Technology [2013DFM10100]
- National Natural Science Foundation of China [61202347, 61272194, 61401385, 61472051, 61373086]
- U.S. National Science Foundation [CMMI-1162482]
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005, cstc2013kjrc-qnrc0079]
- Postdoctoral Science Funded Project of Chongqing [Xm2014043]
- China Postdoctoral Science Foundation [2014M562284]
- Fundamental Research Funds for the Central Universities [106112015CDJXY180005, 106112014CDJZR185503]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120191120030]
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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.
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