Predicting Quality of Services Based on a Two-Stream Deep Learning Model With User and Service Graphs
Peiyun Zhang, Wenjun Huang, Yutong Chen, MengChu Zhou
Abstract
Accurately predicting Quality of Service (QoS) is one of the main challenges in the area of service recommendation, and it has thus attracted much attention in recent years. This field exists many methods, most of which are inspired by collaborative filtering in service recommendation. They predict the missing QoS values of services by collecting the historical information of similar users/services, but their prediction accuracy needs further improvements. This work proposes user and service graphs are proposed for the first time in the field of QoS prediction by exploiting deep relationships among users and services. Based on the graphs, user/service feature vector sets are found via similar users/services. A two-stream deep learning-based prediction model is proposed for service QoS prediction. It has a deep convolutional neural network with two efficient deep convolutional units to deal with user/service feature vectors parallelly. Experiments are carried out to show that the proposed method can achieve better QoS prediction accuracy of services than the existing approaches such as non-negative matrix factorization, probabilistic matrix factorization, covering-based neighbor-hood-aware matrix factorization, neighbor integration deep matrix factorization, and several traditional methods.