NFMF: neural fusion matrix factorisation for QoS prediction in service selection

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...

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Bibliographic Details
Main Authors: Jianlong Xu, Lijun Xiao, Yuhui Li, Mingwei Huang, Zicong Zhuang, Tien-Hsiung Weng, Wei Liang
Format: Article
Language:English
Published: Taylor & Francis Group 2021-07-01
Series:Connection Science
Subjects:
qos prediction
neural network
matrix factorisation
contextual information
multi-task learning
Online Access:http://dx.doi.org/10.1080/09540091.2021.1889975
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Summary: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.
ISSN:0954-0091
1360-0494

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