DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation. (11th May 2022)

• Record Type: Journal Article
• Authors: Li, Kui; Ji, Yi‐mu; Liu, Shang‐dong; Wu, Fei; Yao, Hai‐chang; He, Jing; Liu, Qiang; Liu, Yan‐lan; Shao, Si‐si; You, Shuai
• Is Part Of: Concurrency and computation. Volume 34:Number 18(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-11
• Subjects: low‐rank matrix factorization -- privacy protection -- QoS clustering -- QoS‐aware -- service recommendation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7003
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.