A Context-Aware Approach for Personalised and Adaptive QoS Assessments

Given the importance of QoS (quality of service) properties for distinguishing between functionally-equivalent services and accommodating different user expectations, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach.