Abstract
With the rapidly overwhelming number of services on the internet, QoS-based web service recommendation has become an urgent demand on service-oriented applications. Since there are a large number of missing QoS values in the user historical invocation records, accurately predicting these missing QoS values becomes a hot research issue. However, most existing service QoS prediction research assumes that the transactional process of the service was stable, and its QoS doesn’t change as time goes. In fact, service invocation process is usually affected by many factors (e.g., geographical location, network environment), leading to service invocations with QoS uncertainty. Therefore, QoS prediction based on traditional methods can not exactly adapt to the scenarios in real-world applications. To solve the issue, combined with the collaborative filtering and matrix factorization theory, we propose a novel approach for prediction of uncertain service QoS under the dynamic Internet environment. Extensive experiments have been conducted on a real-world data set and the results demonstrate the effectiveness and applicability of our approach for QoS prediction.
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Acknowledgement
This work was partially supported by Shanghai Natural Science Foundation (No. 18ZR1414400, 17ZR1400200), National Natural Science Foundation of China (No. 61772128, 61303096), Shanghai Sailing Program (No. 16YF1400300), and Fundamental Research Funds for the Central Universities (No. 16D111208).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zou, G. et al. (2019). Neighborhood-Based Uncertain QoS Prediction of Web Services via Matrix Factorization. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_46
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