Skip to main content

Research Repository

Advanced Search

Spatial-temporal data-driven service recommendation with privacy-preservation

Zhang, Xuyun; Qi, Lianyong; Li, Shancang; Wan, Shaohua; Wen, Yiping; Gong, Wenwen


Xuyun Zhang

Lianyong Qi

Shancang Li
Senior Lecturer in Computer Forensics and Security

Shaohua Wan

Yiping Wen

Wenwen Gong


© 2019 Elsevier Inc. The ever-increasing popularity of web service sharing communities have produced a considerable amount of web services that share similar functionalities but vary in Quality of Services (QoS) performances. To alleviate the heavy service selection burden on users, lightweight recommendation ideas, e.g., Collaborative Filtering (CF) have been developed to aid users to select their preferred services. However, existing CF methods often face two challenges. First, service QoS is often context-aware and hence depends on the spatial and temporal information of service invocations heavily. While it requires challenging efforts to integrate both spatial and temporal information into service recommendation decision-making process simultaneously. Second, the location-aware and time-aware QoS data often contain partial sensitive information of users, which raise an emergent privacy-preservation requirement when performing service recommendations. In view of above two challenges, in this paper, we integrate the spatial-temporal information of QoS data and Locality-Sensitive Hashing (LSH) into recommendation domain and bring forth a location-aware and time-aware recommendation approach considering privacy concerns. At last, a set of experiments conducted on well-known WS-DREAM dataset show the feasibility of our approach.


Zhang, X., Qi, L., Li, S., Wan, S., Wen, Y., & Gong, W. (2020). Spatial-temporal data-driven service recommendation with privacy-preservation. Information Sciences, 515, 91-102.

Journal Article Type Article
Acceptance Date Nov 13, 2019
Online Publication Date Nov 28, 2019
Publication Date Apr 1, 2020
Deposit Date Dec 19, 2019
Publicly Available Date Nov 29, 2020
Journal Information Sciences
Print ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 515
Pages 91-102
Keywords Control and Systems Engineering; Theoretical Computer Science; Software; Information Systems and Management; Artificial Intelligence; Computer Science Applications
Public URL
Additional Information This article is maintained by: Elsevier; Article Title: Spatial-temporal data-driven service recommendation with privacy-preservation; Journal Title: Information Sciences; CrossRef DOI link to publisher maintained version:; Content Type: article; Copyright: © 2019 Elsevier Inc. All rights reserved.


You might also like

Downloadable Citations