Journal
INFORMATION SCIENCES
Volume 265, Issue -, Pages 68-84Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.12.007
Keywords
QoS prediction; Collaborative Filtering; Matrix Factorization; Regularization
Categories
Funding
- National Natural Science Foundation of China [61272129]
- National High-Tech Research Program of China [2013AA01A213]
- NewCentury Excellent Talents Program by Ministry of Education of China [NCET-12-0491]
- Zhejiang Provincial Natural Science Foundation of China [LR13F020002]
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Quality-of-Service (QoS) is a fundamental element in Service-Oriented Computing (SOC) domain. At the ongoing age of Web 2.0, predicting the missing QoS values becomes more and more important since it is an indispensable preprocess of numerous service-oriented applications. Previous research works on this task underestimate the importance of users' geographical information, which we argue would contribute to improving prediction accuracy in Web services invocation process. In this paper, we propose a novel collaborative location-based regularization framework (Colbar) to address the problem of personalized QoS prediction. We first leverage the personal geographical and QoS information to identify robust neighborhoods. And then, we collect the wisdom of crowds to construct two location-based regularization terms, which are integrated to build up an unified Matrix Factorization framework. Finally we make intermediate fusions to generate better prediction results. The experimental analysis on a large-scale real-world QoS dataset shows that the prediction accuracy of Colbar outperforms other state-of-the-art approaches in various criteria. (C) 2013 Published by Elsevier Inc.
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