Sri-Xdfm: A Service Reliability Inference Method Based On Deep Neural Network

With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with the help of character encoding and a CNN. Second, the original sum pooling method in xDeepFM is improved with an adaptive pooling method for reducing the information loss of the pooling operations when learning linear information. Finally, an inter-attention mechanism is applied in the DNN to learn the relationship between the user and the service data when learning nonlinear information. Experiments that were conducted on a public real-world web service data set confirm the effectiveness and superiority of the SRI-XDFM.