Web Service Recommendation Based on QoS Analysis
| Autor: | Mindong Xin, Xing Xing, Qiuyang Han, Zhichun Jia |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 2219:012053 |
| ISSN: | 1742-6596 1742-6588 |
| Popis: | To correctly recommend the web services and help users choose more appropriate web services, a QoS analysis and recommendation method based on radial basis function (RBF) neural network is proposed (this method is referred to as WSR-RBF). This method constructs an optimized RBF neural network to analyze the correlation between qualities of service attributes and uses a set of irrelevant attributes to accurately predict quality of service. To find the number of hidden layer nodes in our proposed model and determine the center value of the hidden layer in a short time, we propose a clustering algorithm combining Affinity Propagation and K-means. The experimental results show that the proposed method has low error values on three data sets, which verifies its good recommendation effect. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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