Joint Deep Networks Based Multi-Source Feature Learning for QoS Prediction

Abstract

The ever-increasing diversity and dynamic of cloud environment pose many challenges on QoS prediction in service recommendation. One such challenge is how to extract and learn deep features of users/services from multi-source information to improve prediction accuracy. In this article, we propose a novel Joint Deep Networks based Multi-source Feature Learning(JDNMFL) framework for QoS prediction. JDNMFL has two parts: Multi-source Feature Extraction and Feature Interaction Learning. In the first part, a latent factor embedding method is first proposed to capture implicit features from QoS matrix, and then the multi-source features, combined by explicit features from WSDL(Web Services Description Language) document and contextual data as well as implicit features, are extracted based on the combination of matrix factorization and neural networks. In the second part, the CNN(Convolutional Neural Network)-based joint deep networks are built to learn both local and global high-order feature interactions, and to complete the final QoS prediction based on mixed features. Experimental results demonstrate that our JDNMFL approach can not only extract and integrate implicit and explicit features of various multi-source data, but also can learn feature sequence and feature interactions, so that it is very effective in improving the accuracy of QoS prediction with sparse data.