Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation
Refereed conference paper presented and published in conference proceedings


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AbstractWeb service recommendation has recently drawn much attention with the growing amount of Web services. Previous work usually exploits the collaborative filtering techniques for Web service recommendation, but suffers from the data sparsity problem that leads to inaccurate results. Our analysis on a real-world Quality of Service (QoS) dataset shows that there is a hidden correlation among users and services. We define such hidden correlation with an asymmetric matrix (namely asymmetric correlation), in which each entry presents the hidden correlation between a user pair or between a service pair. The goal of this work is to employ such asymmetric correlation among users and services to alleviate the data sparsity problem and further enhance the prediction accuracy in service recommendation. Specifically, we propose an asymmetric correlation regularized matrix factorization (MF) framework, in which asymmetric correlation and asymmetric correlation propagation have been naturally integrated. Finally, experimental results on a well-known real-world QoS dataset validate that the use of asymmetric correlation among users and services is effective in improving prediction accuracy for Web service recommendation.
Acceptance Date27/06/2016
All Author(s) ListQi Xie , Shenglin Zhao, Zibin Zheng, Jieming Zhu, Michael R. Lyu
Name of Conference2016 IEEE International Conference on Web Services
Start Date of Conference27/06/2016
End Date of Conference02/07/2016
Place of ConferenceSan Francisco
Country/Region of ConferenceUnited States of America
Proceedings Title2016 IEEE International Conference on Web Services (ICWS)
Year2016
Month6
PublisherInstitute of Electrical and Electronics Engineers
Pages204 - 211
ISBN9781509026760
LanguagesEnglish-United States
KeywordsCorrelation, Quality of service, Web services, Symmetric matrices, Measurement, Computer science, Collaboration

Last updated on 2021-17-01 at 01:21