A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

Sensors
Li KuangYujia Zhu

Abstract

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify un...Continue Reading

Citations

Dec 24, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Jingpu Zhang, Lei Deng
Sep 26, 2019·Frontiers in Bioengineering and Biotechnology·Zhibin LvQuan Zou
Mar 3, 2020·Frontiers in Genetics·Chunyu WangMaozu Guo
Jul 25, 2021·Sensors·Zhiqiang Pan, Honghui Chen

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Methods Mentioned

BETA
RAP

Software Mentioned

TAP
Python
GURAP
WSPred
WSDream
GURAP ( Reputation Aware Prediction )
UIPCC
CURA
GUIPCC
Windows

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