Web Service Quality Prediction Method Based on Recurrent Neural Network

For web services, QoS (Quality of Service, quality of service) is an important indicator for judging whether a web service is efficient. How to better predict the QoS value of the service to make appropriate service recommendations is the entire recommendation system and Issues that are being discussed in the service forecasting academia. At the same time, the timeliness and time relevance of QoS values are also affecting the prediction accuracy of Web services. A large amount of QoS data has potentially time-related attributes. This provides a new inspiration and thinking for service forecasting. Add the time characteristics of the data to the learning of the predictive model. Inspired by these factors, this paper proposes a deep neural network combination model that is sensitive to the time characteristics of QoS. At the same time, based on the final experimental results, the model proposed in this paper has obvious effects on the prediction of QoS values with time attributes.