TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
An expanded Matrix Factorization model for real-time Web service QoS prediction |
| 영문제목(English Title) |
An expanded Matrix Factorization model for real-time Web service QoS prediction |
| 저자(Author) |
Jinsheng Hao
Guoping Su
Xiaofeng Han
Wei Nie
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| 원문수록처(Citation) |
VOL 15 NO. 11 PP. 3913 ~ 3934 (2021. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms. |
| 키워드(Keyword) |
Real-time QoS prediction
matrix factorization
clustering
K-means
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| 파일첨부 |
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