TY - JOUR
T1 - Spatial-temporal data-driven service recommendation with privacy-preservation
AU - Qi, Lianyong
AU - Zhang, Xuyun
AU - Li, Shancang
AU - Wan, Shaohua
AU - Wen, Yiping
AU - Gong, Wenwen
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Collaborative Filtering
KW - Locality-Sensitive Hashing
KW - Privacy-preservation
KW - Service recommendation
KW - Spatial-temporal QoS
UR - http://www.scopus.com/inward/record.url?scp=85076253763&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.11.021
DO - 10.1016/j.ins.2019.11.021
M3 - Article
VL - 515
SP - 91
EP - 102
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -