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....
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-09-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/17/9/2059 |
id |
doaj-a959a541ee5d49ab8fa86a33e27de6b0 |
---|---|
record_format |
Article |
spelling |
doaj-a959a541ee5d49ab8fa86a33e27de6b02020-11-25T01:05:47ZengMDPI AGSensors1424-82202017-09-01179205910.3390/s17092059s17092059Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical SystemsYuyu Yin0Fangzheng Yu1Yueshen Xu2Lifeng Yu3Jinglong Mu4School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310019, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310019, ChinaSchool of Software, Xidian University, Xi’an 710071, ChinaHithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, ChinaFushun Power Supply Branch, State Grid Liaoning Electric Power Supply Co., Ltd., Fushun 113008, ChinaCyber-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.https://www.mdpi.com/1424-8220/17/9/2059cyber-physical systemsservice recommendationQoS predictionnetwork locationrandom walk |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuyu Yin Fangzheng Yu Yueshen Xu Lifeng Yu Jinglong Mu |
spellingShingle |
Yuyu Yin Fangzheng Yu Yueshen Xu Lifeng Yu Jinglong Mu Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems Sensors cyber-physical systems service recommendation QoS prediction network location random walk |
author_facet |
Yuyu Yin Fangzheng Yu Yueshen Xu Lifeng Yu Jinglong Mu |
author_sort |
Yuyu Yin |
title |
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_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_sort |
network location-aware service recommendation with random walk in cyber-physical systems |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-09-01 |
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. |
topic |
cyber-physical systems service recommendation QoS prediction network location random walk |
url |
https://www.mdpi.com/1424-8220/17/9/2059 |
work_keys_str_mv |
AT yuyuyin networklocationawareservicerecommendationwithrandomwalkincyberphysicalsystems AT fangzhengyu networklocationawareservicerecommendationwithrandomwalkincyberphysicalsystems AT yueshenxu networklocationawareservicerecommendationwithrandomwalkincyberphysicalsystems AT lifengyu networklocationawareservicerecommendationwithrandomwalkincyberphysicalsystems AT jinglongmu networklocationawareservicerecommendationwithrandomwalkincyberphysicalsystems |
_version_ |
1725193235800784896 |