NFMF: neural fusion matrix factorisation for QoS prediction in service selection
Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-07-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1889975 |
| _version_ | 1797684058165084160 |
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| author | Jianlong Xu Lijun Xiao Yuhui Li Mingwei Huang Zicong Zhuang Tien-Hsiung Weng Wei Liang |
| author_facet | Jianlong Xu Lijun Xiao Yuhui Li Mingwei Huang Zicong Zhuang Tien-Hsiung Weng Wei Liang |
| author_sort | Jianlong Xu |
| collection | DOAJ |
| description | Selecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method. |
| first_indexed | 2024-03-12T00:23:53Z |
| format | Article |
| id | doaj.art-34bca9fb69e94db89042ae1074e2c191 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:53Z |
| publishDate | 2021-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-34bca9fb69e94db89042ae1074e2c1912023-09-15T10:47:59ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-07-0133375376810.1080/09540091.2021.18899751889975NFMF: neural fusion matrix factorisation for QoS prediction in service selectionJianlong Xu0Lijun Xiao1Yuhui Li2Mingwei Huang3Zicong Zhuang4Tien-Hsiung Weng5Wei Liang6Shantou UniversityGuangzhou College of Technology and BusinessShantou UniversityShantou UniversityShantou UniversityProvidence UniversityHunan UniversitySelecting suitable web services based on the quality-of-service (QoS) is essential for developing high-quality service-oriented applications. A critical step in this direction is acquiring accurate, personalised QoS values of web services. As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. Moreover, we consider context bias and employ multi-task learning to reduce prediction error and improve the predicted performance. Furthermore, we conducted extensive experiments in a large-scale real-world QoS dataset, and the experimental results verify the effectiveness of our proposed method.http://dx.doi.org/10.1080/09540091.2021.1889975qos predictionneural networkmatrix factorisationcontextual informationmulti-task learning |
| spellingShingle | Jianlong Xu Lijun Xiao Yuhui Li Mingwei Huang Zicong Zhuang Tien-Hsiung Weng Wei Liang NFMF: neural fusion matrix factorisation for QoS prediction in service selection Connection Science qos prediction neural network matrix factorisation contextual information multi-task learning |
| title | NFMF: neural fusion matrix factorisation for QoS prediction in service selection |
| title_full | NFMF: neural fusion matrix factorisation for QoS prediction in service selection |
| title_fullStr | NFMF: neural fusion matrix factorisation for QoS prediction in service selection |
| title_full_unstemmed | NFMF: neural fusion matrix factorisation for QoS prediction in service selection |
| title_short | NFMF: neural fusion matrix factorisation for QoS prediction in service selection |
| title_sort | nfmf neural fusion matrix factorisation for qos prediction in service selection |
| topic | qos prediction neural network matrix factorisation contextual information multi-task learning |
| url | http://dx.doi.org/10.1080/09540091.2021.1889975 |
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