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

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....

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Detalles Bibliográficos
Autores principales: Yin, Yuyu, Yu, Fangzheng, Xu, Yueshen, Yu, Lifeng, Mu, Jinglong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621120/
https://www.ncbi.nlm.nih.gov/pubmed/28885602
http://dx.doi.org/10.3390/s17092059
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author Yin, Yuyu
Yu, Fangzheng
Xu, Yueshen
Yu, Lifeng
Mu, Jinglong
author_facet Yin, Yuyu
Yu, Fangzheng
Xu, Yueshen
Yu, Lifeng
Mu, Jinglong
author_sort Yin, Yuyu
collection PubMed
description 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.
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spelling pubmed-56211202017-10-03 Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems Yin, Yuyu Yu, Fangzheng Xu, Yueshen Yu, Lifeng Mu, Jinglong Sensors (Basel) Article 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. MDPI 2017-09-08 /pmc/articles/PMC5621120/ /pubmed/28885602 http://dx.doi.org/10.3390/s17092059 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yin, Yuyu
Yu, Fangzheng
Xu, Yueshen
Yu, Lifeng
Mu, Jinglong
Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title_full Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title_fullStr Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title_full_unstemmed Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title_short Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
title_sort network location-aware service recommendation with random walk in cyber-physical systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621120/
https://www.ncbi.nlm.nih.gov/pubmed/28885602
http://dx.doi.org/10.3390/s17092059
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