Research Article
EFMLP: A Novel Model for Web Service QoS Prediction
@INPROCEEDINGS{10.1007/978-3-030-67540-0_22, author={Kailing Ye and Huiqun Yu and Guisheng Fan and Liqiong Chen}, title={EFMLP: A Novel Model for Web Service QoS Prediction}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Embedding model Factorization machine Neural network QoS prediction}, doi={10.1007/978-3-030-67540-0_22} }- Kailing Ye
Huiqun Yu
Guisheng Fan
Liqiong Chen
Year: 2021
EFMLP: A Novel Model for Web Service QoS Prediction
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_22
Abstract
With the emergence of service-oriented architecture, quality of service (QoS) has become a crucial factor in describing the non-functional characteristics of Web services. In the real world, the user only requests limited Web services, the QoS record of Web services is sparsity. In this paper, we propose an approach named factorization machine and multi-layer perceptron model based on embedding technology (EFMLP) to solve the problem of sparsity and high dimension. First, the input data will be sent to embedding layer to reduce the data dimension. Then, the embedded feature vector will send to the factorization machine. After that, the first-order and second-order weights of the factorization machine are used as the initial weights of the first layer of the multi-layer perceptron. And the multi-layer perceptron is trained to adjust the weights. Finally, 1,974,675 pieces of data from an open dataset is used as experiment data to validate the model, and the result shows that our EFMLP model can predict QoS value accurately on the client side.
