An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Sensors

Xuyun Zhang¹  Ruili Wang²  Shunmei Meng³  Xiaolong Xu⁴  Zhili Zhou⁴  Lianyong Qi⁵  Wanchun Dou⁶ 

[1] Department of Electrical and Computer Engineering, University of Auckland, Auckland 1023, New Zealand;Institute of Natural and Mathematical Sciences, Massey University, Auckland 0745, New Zealand;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China;School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China;State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;

关键词: service recommendation;    privacy-preservation;    failure;    exception handling;    converse Locality-Sensitive Hashing;   

DOI  :  10.3390/s18072037

来源: DOAJ

【 摘 要 】

Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

【 授权许可】

Unknown