NCRL: Neighborhood-Based Collaborative Residual Learning for Adaptive QoS Prediction

How to accurately predict vacant QoS has become a fundamental issue for service-oriented downstream tasks. However, most QoS prediction approaches based on model learning fail to discriminatively capture the latent feature representations of a user and a service, since they either leverage the shallow neural network such as MLP or take advantage of insufficient location information. Moreover, collaborative relationships of similar neighborhood have not been fully taken into account together with prediction model learning. To address these issues, we propose a novel framework for adaptive QoS prediction named Neighborhood-based Collaborative Residual Learning (NCRL). Location-aware two-tower deep residual network is designed to achieve neural QoS prediction by extracting latent features of users and services, which are fed to generate similar neighborhood for collaborative prediction based on historical QoS invocations. They are integrally combined to perform adaptive QoS prediction. Extensive experiments are conducted based on a large-scale real-world QoS dataset called WS-DREAM with almost 2,000,000 historical QoS invocations. The results indicate that NCRL can remarkably outperform state-of-the-art competing baselines.