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Open AccessArticle

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems

by Yuyu Yin 1,2,†, Fangzheng Yu 1,2, Yueshen Xu 3,*,†, Lifeng Yu 4 and Jinglong Mu 5
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310019, China
Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou 310019, China
School of Software, Xidian University, Xi’an 710071, China
Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, China
Fushun Power Supply Branch, State Grid Liaoning Electric Power Supply Co., Ltd., Fushun 113008, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work, and they are co-first authors.
Sensors 2017, 17(9), 2059;
Received: 16 July 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 8 September 2017
Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction. View Full-Text
Keywords: cyber-physical systems; service recommendation; QoS prediction; network location; random walk cyber-physical systems; service recommendation; QoS prediction; network location; random walk
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Yin, Y.; Yu, F.; Xu, Y.; Yu, L.; Mu, J. Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems. Sensors 2017, 17, 2059.

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