Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
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AbstractWith the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users. We first apply the concept of user-collaboration for the web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 web service invocations from 339 service users on 5,825 real-world web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our web service QoS data set provides valuable real-world data for future research.
All Author(s) ListZheng ZB, Ma H, Lyu MR, King I
Journal nameIEEE Transactions on Services Computing
Volume Number6
Issue Number3
Pages289 - 299
LanguagesEnglish-United Kingdom
Keywordsmatrix factorization; QoS prediction; user-collaboration; Web service
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING

Last updated on 2020-24-11 at 00:36