A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation
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AbstractDue to the popularity of service-oriented architectures for various distributed systems, an increasing number of Web services have been deployed all over the world. Recently, Web service recommendation became a hot research topic, one that aims to accurately predict the quality of functional satisfactory services for each end user. Generally, the performance of Web service changes over time due to variations of service status and network conditions. Instead of employing the conventional temporal models, we propose a novel spatial-temporal QoS prediction approach for time-aware Web service recommendation, where a sparse representation is employed to model QoS variations. Specifically, we make a zero-mean Laplace prior distribution assumption on the residuals of the QoS prediction, which corresponds to a Lasso regression problem. To effectively select the nearest neighbor for the sparse representation of temporal QoS values, the geolocation of web service is employed to reduce searching range while improving prediction accuracy. The extensive experimental results demonstrate that the proposed approach outperforms state-of-art methods with more than 10% improvement on the accuracy of temporal QoS prediction for time-aware Web service recommendation.
All Author(s) ListWang XY, Zhu JK, Zheng ZB, Song WJ, Shen YH, Lyu MR
Journal nameACM Transactions on the Web
Year2016
Month2
Day1
Volume Number10
Issue Number1
PublisherAssociation for Computing Machinery (ACM)
ISSN1559-1131
eISSN1559-114X
LanguagesEnglish-United Kingdom
KeywordsAlgorithms; Design, Performance, QoS prediction, service recommendation, spatial-temporal QoS prediction, Web service
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; Computer Science, Software Engineering

Last updated on 2021-21-01 at 00:52