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

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 5; p. 1556
• Main Authors: Kuang, Li, Yu, Long, Huang, Lan, Wang, Yin, Ma, Pengju, Li, Chuanbin, Zhu, Yujia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.05.2018; MDPI
• Subjects: Cold starts; Collaboration; Cyber-physical systems; Dirichlet problem; Filtration; Reputations; cyber-physical systems; QoS prediction; collaborative filtering; user reputation; data sparsity; geographical neighbors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18051556

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 untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.