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A graph-based QoS prediction approach for web service recommendation

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Abstract

With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in both industry and academia. However, it is hard to make an accurate recommendation using sparse QoS data, which makes QoS prediction a growing concern in the context of web service recommendation. In this research, a novel Graph-based Matrix Factorization approach(GMF) is proposed for QoS prediction. First, a concept of integrated-graph is put forward to consolidate multi-source information from user–aware context and service-aware context, and to deep mine potential relationships based on QoS matrix. Furthermore, the integrated-graph is divided into several sub-graphs by cutting insignificant edges to reduce noises and strengthen interactions between users and services. Based on the local information of each sub-graph and the global information of integrated-graph, a Gaussian Mixture Model(GMM) of QoS value is built as a fusion method to combine local and global information adaptively and to complete final QoS prediction. The extensive experimental analysis on a publicly available dataset indicate that our graph-based method is both accurate and practical.

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Acknowledgments

This work is supported by National Natural Science Foundation of China(61672086, 51827813), and Fundamental Research Funds for the Central Universities(2019JBM025, 2019JBZ104).

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Authors and Affiliations

  1. School of Computer and Information Technology in Beijing Jiaotong University, Beijing, 100044, China

    Zhenhua Chang & Youhao Xia

  2. School of Computer and Information Technology in Beijing Jiaotong University and the Beijing Key Lab of Traffic Data Analysis and Mining, Beijing, 100044, China

    Ding Ding

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  1. Zhenhua Chang
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  2. Ding Ding
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  3. Youhao Xia
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Correspondence to Ding Ding.

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Chang, Z., Ding, D. & Xia, Y. A graph-based QoS prediction approach for web service recommendation. Appl Intell 51, 6728–6742 (2021). https://doi.org/10.1007/s10489-020-02120-5

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