@inproceedings{056cba0ca2404eecaeb7e1be9e37a817,
title = "A Hessian-Free Optimization-Based Approach to Latent-Factor-Based QoS Predictors with High Accuracy",
abstract = "Latent-factor-based Quality-of-Service predictors can achieve high prediction accuracy and good scalability. However, most of them are based on first-order models that cannot well deal with their target problem that is inherently non-convex. Since second-order approaches have proven to be effective to such problems, this work proposes to implement a second-order predictor with an aim to achieve the high accuracy unlikely obtained by any existing methods. To do so, this work adopts the principle of Hessian-free optimization and successfully avoids the usage of a Hessian matrix by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Experimental results on two industrial QoS datasets indicate that the newly proposed predictor is highly accurate with fine computational efficiency.",
keywords = "Big Data, Hessian-free optimization, QoS, Web-Services, latent-factor, second-order",
author = "Xin Luo and Yunni Xia and Qingsheng Zhu and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 ; Conference date: 09-10-2015 Through 12-10-2015",
year = "2016",
month = jan,
day = "12",
doi = "10.1109/SMC.2015.289",
language = "English (US)",
series = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1639--1644",
booktitle = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",
address = "United States",
}