Dynamic QoS Prediction Based on Attention Mechanism and Recurrent Neural Network
Yingxue Wang, Qin Lu, Yichao Wang, Mengwei Wu, Weixiao Li · 2022
With the growth of science and technology, there are more and more services in the network, and their functions similar. It has become crucial to figure out how to recommend real-time personalized services based on user and service history. Quality of Service (QoS), as a noncritical attribute of Web services, it is a vital criterion to measure the usage performance and utility of service. The dynamic prediction of QoS is a key factor in the recommendation process. In this paper, a new time-aware QoS prediction model is proposed. It extracte global features while considering its dynamics, it capture the implicit features within a time period using Convolutional Neural Networks(CNN) and Attention Mechanisms. The model not only takes into account the impact of time, but also better learns the implicit information between users and services during the time interval, forming global features within time. It study QoS at various times from the entire time period, capture key features, thereby make dynamic QoS prediction. The proposed model has better results by extensive experiments on real dataset.