Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have dem...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-10, Vol.2022, p.1-16
Main Authors: Bu, Hualong, Xia, Jing, Wu, Qilin, Chen, Liping
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Cloud computing
Clustering
Deep learning
Embedding
Graph theory
Internet of Things
Internet service providers
Neighborhoods
Neural networks
Predictions
Quality of service
Semantics
Similarity
Sparsity
Teaching methods
Web services
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Summary:Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/9240843