A Deep-Learning Model for Service QoS Prediction Based on Feature Mapping and Inference
Peiyun Zhang, Jigang Ren, Wenjun Huang, Yutong Chen, Qinglin Zhao, Haibin Zhu
Abstract
Quality of Service (QoS) prediction is a crucial issue in service recommendation, which has been widely studied in the past few years. It faces several challenges, including improving QoS prediction accuracy. Can one extract and use deep features of users and services to improve it? This work answers this question by proposing a deep-learning model for service QoS prediction. In this model, a feature mapping and inference network is first designed to obtain high-dimensional feature matrices of users and services, which can enhance data flow information and reflect the deep relationships among users and services. Then, feature compensation blocks are designed to compensate for the possible loss of feature information in feature mapping and inference. Finally, a QoS prediction network is constructed to fuse the obtained feature matrices to predict QoS values. Experimental results show that the proposed method can achieve higher prediction accuracy than ten typical and representative methods, thus advancing the state of the art in QoS prediction.