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Time-aware distributed service recommendation with privacy-preservation

Xu, Xiaolong; He, Qiang; Qi, Lianyong; Wang, Ruili; Hu, Chunhua; Li, Shancang

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Xiaolong Xu

Qiang He

Lianyong Qi

Ruili Wang

Chunhua Hu

Shancang Li


© 2018 As a promising way to extract insightful information from massive data, service recommendation has gained ever-increasing attentions in both academic and industrial areas. Recently, the Locality-Sensitive Hashing (LSH) technique is introduced into service recommendation to pursue high recommendation efficiency and the capability of privacy-preservation, especially when the historical service quality (QoS) data used to make recommendation decisions are distributed across different platforms. However, existing LSH-based service recommendation approaches often face the following challenge: they often assume that the QoS data for service recommendation are static and unique, without considering the influence of dynamic context (e.g., time) on QoS. In view of this challenge, we extend the traditional LSH technique to incorporate the time factor and further propose a novel time-aware and privacy-preserving service recommendation approach based on LSH. Finally, we conduct extensive experiments on a large-scale real-world dataset, i.e., WS-DREAM, to validate the effectiveness and efficiency of our proposal. The experiment results show that our approach achieves a good tradeoff between recommendation accuracy and efficiency while guaranteeing privacy-preservation.

Journal Article Type Article
Acceptance Date Nov 25, 2018
Online Publication Date Nov 24, 2018
Publication Date Apr 1, 2019
Deposit Date Nov 28, 2018
Publicly Available Date Nov 25, 2019
Journal Information Sciences
Print ISSN 0020-0255
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 480
Pages 354-364
Keywords distributed service recommendation, privacy-preservation, time, locality-sensitive hashing
Public URL
Publisher URL
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here:
Contract Date Nov 28, 2018


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