Design And Implementation Of Web Service QoS Prediction Subsystem Based On Improved XDeepFM Model

In recent years,with the gradual improvement of Internet infrastructure and the rapid development of cloud services,major enterprises and organizations have launched a large number of web services.The application scenarios and quantity of web services are increasing at an extremely rapid rate.The vigorous development of web services has also introduced new problems.The problem of how to choose the most suitable web service for a specific user from a large number of homogeneous web services has gradually become prominent.Quality of web service prediction has gradually become the research focus of scholars at home and abroad.The existing various types of web service quality prediction algorithms mainly use Bayesian algorithms,Clustering algorithms,Collaborative filtering algorithms,Matrix factorization algorithms,and codec models.These types of algorithms have problems such as difficulty in adapting to a large number of sparse data sets,only applicable in the point-to-point web service quality prediction,and impossible to balance low-dimensional and high-dimensional web service quality information.As an excellent deep neural network model,the xDeepFM model is widely used in a variety of prediction problems because it does not require manual feature combination,is suitable for sparse data sets,and takes into account high and low dimensional information.However,it still has shortcomings in the specific application scenario of network service quality prediction.First,its randomly initialized feature embedding method will lose a lot of user and service information in the embedding layer;Secondly,its compressed interactive neural network module for learning high-dimensional information uses a simple summation pooling technique,resulting in its high-dimensional information extraction ability is poor;Finally,its deep neural network module for learning low-dimensional information simply uses several fully connected neural layers,and its structure is too simple,resulting in poor low-dimensional information learning ability.Therefore,the demand for a web service quality prediction algorithm that can better solve the above problems is about to emerge.In response to the above problems,this paper proposes a web service quality prediction algorithm based on the improved xDeepFM model,and designs and implements a web service quality prediction subsystem based on the improved xDeepFM model.The system can efficiently and accurately realize the network web quality prediction function.The research work of this paper is as follows:(1)A web service quality prediction algorithm based on the improved xDeepFM model is proposed.First of all,in view of the problem that autocorrelation information is easy to lose during feature embedding of user and network service quality data,this paper proposes and uses a feature embedding method based on character encoding and convolutional neural network.Secondly,in view of the poor ability of the model to extract high-dimensional information,this paper improves the pooling layer of the compressed interactive neural network module,and proposes an adaptive pooling method that can better abstract high-dimensional information.Then,in order to solve the problem of poor low-dimensional information learning ability of the model,this paper introduces the idea of attention in the deep neural network module,and proposes a low-dimensional service quality information learning model based on attention mechanism and neural network,which improves the model's ability to learn low-dimensional information.Finally,we conducted simulation experiments on the algorithm designed in this paper.Compared with traditional web service quality prediction algorithms,the algorithm proposed in this paper has considerable improvement in indicators such as MAE and RMSE.(2)In response to the actual application needs of users for data preprocessing,data manipulation,web service quality prediction and web service recommendation,this paper designs and implements a web service quality prediction subsystem based on the improved xDeepFM model based on the React and Spring Boot framework.This article describes in detail the demand analysis,system architecture,and system module processing flow of the web service quality prediction subsystem,and designs and implements module functions such as data preprocessing,data operation,network service quality prediction and web service recommendation.Finally,we conducted a detailed test on the system from both functions and performance.The test results show that the web service quality prediction subsystem designed in this article can accurately and efficiently perform web service quality data preprocessing,data manipulation,web service quality prediction and web service recommendation operations.It meets the functional and performance requirements of the web service quality prediction subsystem.