Research On Qos Prediction Method Based On Collaborative Filtering In Internet Of Things Environment

With the rapid growth of IoT access users and terminal devices,the differentiation of user requirements is becoming more and more obvious,and users have higher requirements for the quality of service(QoS)of the network.How to realize the rational distribution of IoT resources and the personalized service recommendation for users through QoS prediction has gradually become a research hotspot of scholars.The recommendation system can mine the association between users through behavior data,and has the advantage of being highly explanatory,and has been widely used in QoS prediction.However,the traditional QoS prediction algorithm does not consider the real-time variability of QoS in the IoT environment,and the prediction is improved by the improvement of a single similarity calculation method,resulting in low prediction accuracy.In order to improve the QoS prediction effect,the paper uses collaborative filtering and artificial intelligence method to study QoS prediction methods in the Internet of Things environment.The specific research work is as follows:(1)Research on QoS prediction method based on Service2 vec.Firstly,the service item is divided into high-response service items and non-high-response service items through the threshold.For the non-high-response service item set,a Service2 vec method is designed to mine the potential features of the user's dynamic call service items and construct the service item vector.The user similarity is calculated for the user who invokes the high response service item.Finally,the QoS prediction method based on service item collaborative filtering is adopted for the non-high response service item,and the QoS prediction method based on user collaborative filtering is adopted for the high response service item.(2)Research on QoS prediction method based on AutoEncoder.This paper designs a framework of QoS prediction method based on AutoEncoder.Apply the AutoEncoder method to the response time matrix of the user-service item,obtain the user's feature vector,perform spectral clustering on it,integrate the influence of geographical factors,perform K-Means clustering on the user's geographic location,and finally combine the two.The weights are used for QoS prediction.(3)According to the two QoS prediction methods proposed in this paper,the control experiments are selected respectively,and experiments are carried out in the real WS-DREAM dataset.Experiments show that compared with the comparative QoSprediction methods,the two QoS prediction methods proposed in this paper are obtained.Better prediction results.In view of the difference between services in the IoT environment and traditional environment services,two QoS prediction methods are proposed.Compared with the previous QoS prediction research,the proposed QoS prediction method can effectively improve the QoS prediction accuracy,and the network resources in the IoT environment.Reasonable allocation and recommendation for personalized service recommendations for users in the IoT environment are of great significance.