A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation
Publication in refereed journal


摘要Due 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.
著者Wang XY, Zhu JK, Zheng ZB, Song WJ, Shen YH, Lyu MR
期刊名稱ACM Transactions on the Web
出版社Association for Computing Machinery (ACM)
關鍵詞Algorithms; Design, Performance, QoS prediction, service recommendation, spatial-temporal QoS prediction, Web service
Web of Science 學科類別Computer Science; Computer Science, Information Systems; Computer Science, Software Engineering

上次更新時間 2021-03-03 於 00:59