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QoS Prediction of Web Services Based on a Two-Level Heterogeneous Graph Attention Network

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Bibliographic Details
Title: QoS Prediction of Web Services Based on a Two-Level Heterogeneous Graph Attention Network
Authors: Shengkai Lv, Fangzhou Yi, Peng He, Cheng Zeng
Source: IEEE Access, Vol 10, Pp 1871-1880 (2022)
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2022.
Publication Year: 2022
Subject Terms: QoS prediction, 0202 electrical engineering, electronic engineering, information engineering, service computing, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Heterogeneous graph neural network, attention mechanism, TK1-9971
Description: Quality of Service (QoS) prediction for Web services is a hot research topic in the field of services computing. Recently, representation learning of heterogeneous networks has attracted much attention, and specifically the relationship between users and services, as a typical heterogeneous network in which heterogeneity and rich semantic information provide a new perspective for QoS prediction. This paper proposes a novel QoS Prediction scheme based on a heterogeneous graph attention network. Our method first unitizes the user’s location information to construct an attributed user-service network. Then, considering the difference between nodes and links in the latter network, we model a heterogeneous graph neural network based on a hierarchical attention machine (HGN2HIA) that includes node- and semantic-level attentions. Specifically, node-level attention aims to learn the importance between a node and its meta-path-based neighbors, while semantic-level attention learns the importance of different meta-paths. Finally, user embedding will be generated by aggregating features from meta-path-based neighbors in a hierarchical manner, used for QoS prediction. Experimental results on the public WS-Dream dataset demonstrate the superior performance of the proposed model over the current state-of-the-art methods, with NMAE and RMSE metrics reduced by at least 2.56% and 1.3%, respectively. Furthermore, the experimental results highlight that node-level attention contributes more than semantic-level. Overall, we demonstrate that introducing these attention levels improves the QoS prediction performance.
Document Type: Article
ISSN: 2169-3536
DOI: 10.1109/access.2021.3138127
Access URL: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09662349.pdf
https://doaj.org/article/8972c2da60f349e08a69bd3258031818
Rights: CC BY NC ND
Accession Number: edsair.doi.dedup.....cf98b1ce11d85e3e1d93cd4824cce64c
Database: OpenAIRE
Description
Abstract:Quality of Service (QoS) prediction for Web services is a hot research topic in the field of services computing. Recently, representation learning of heterogeneous networks has attracted much attention, and specifically the relationship between users and services, as a typical heterogeneous network in which heterogeneity and rich semantic information provide a new perspective for QoS prediction. This paper proposes a novel QoS Prediction scheme based on a heterogeneous graph attention network. Our method first unitizes the user’s location information to construct an attributed user-service network. Then, considering the difference between nodes and links in the latter network, we model a heterogeneous graph neural network based on a hierarchical attention machine (HGN2HIA) that includes node- and semantic-level attentions. Specifically, node-level attention aims to learn the importance between a node and its meta-path-based neighbors, while semantic-level attention learns the importance of different meta-paths. Finally, user embedding will be generated by aggregating features from meta-path-based neighbors in a hierarchical manner, used for QoS prediction. Experimental results on the public WS-Dream dataset demonstrate the superior performance of the proposed model over the current state-of-the-art methods, with NMAE and RMSE metrics reduced by at least 2.56% and 1.3%, respectively. Furthermore, the experimental results highlight that node-level attention contributes more than semantic-level. Overall, we demonstrate that introducing these attention levels improves the QoS prediction performance.
ISSN:21693536
DOI:10.1109/access.2021.3138127