Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing

To assess the quality of services (QoS) in service selection, collaborative service QoS prediction has recently garnered increasing attention. They focus on exploring the historical QoS information generated by interactions between users and services. However, they may suffer from the data sparsity issue because interactions between users and services are usually sparse in real scenarios. They also seldom consider the network environments of users and services, which surely will affect cloud service QoS. To address the data sparsity issue and improve the QoS prediction accuracy, the following paper proposes a collaborative QoS prediction method with location-based data smoothing. The method first computes neighborhoods of users and services based on their locations which provide a basis for data smoothing. It then combines user-based and service-based collaborative filtering techniques to make QoS predictions. Experiments conducted using a real service invocation dataset validate the performance of the proposed QoS prediction method. Copyright (C) 2015 John Wiley & Sons, Ltd.\