Research Article
Neighborhood-Based Uncertain QoS Prediction of Web Services via Matrix Factorization
@INPROCEEDINGS{10.1007/978-3-030-12981-1_46, author={Guobing Zou and Shengye Pang and Pengwei Wang and Huaikou Miao and Sen Niu and Yanglan Gan and Bofeng Zhang}, title={Neighborhood-Based Uncertain QoS Prediction of Web Services via Matrix Factorization}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Service-oriented computing Uncertain QoS prediction Collaborative filtering Matrix factorization}, doi={10.1007/978-3-030-12981-1_46} }- Guobing Zou
Shengye Pang
Pengwei Wang
Huaikou Miao
Sen Niu
Yanglan Gan
Bofeng Zhang
Year: 2019
Neighborhood-Based Uncertain QoS Prediction of Web Services via Matrix Factorization
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_46
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.
