App.locky: users' context collecting platform for context-aware application recommendation
Proceedings of the 2nd international workshop on Ubiquitous crowdsouring
Personalized Reliability Prediction of Web Services
ACM Transactions on Software Engineering and Methodology (TOSEM)
Composite Service Recommendation Based on Bayes Theorem
International Journal of Web Services Research
Trust services-oriented multi-objects workflow scheduling model for cloud computing
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Prediction of atomic web services reliability based on k-means clustering
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
A Social-Aware Service Recommendation Approach for Mashup Creation
International Journal of Web Services Research
Predicting unknown QoS value with QoS-Prophet
Proceedings Demo & Poster Track of ACM/IFIP/USENIX International Middleware Conference
Modelling and exploring historical records to facilitate service composition
International Journal of Web and Grid Services
Colbar: A collaborative location-based regularization framework for QoS prediction
Information Sciences: an International Journal
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With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.