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A Context-aware Approach for Personalised and Adaptive QoS Assessments

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationProceeding of the 13th International Conference on Service Oriented Computing
Subtitle of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages9
ISBN (Print)9783662486153
Publication statusPublished - Nov 2015
Eventinternational conference on service oriented computing - Goa, India
Duration: 16 Nov 201619 Nov 2018


Conferenceinternational conference on service oriented computing


  • paper160

    paper160.pdf, 549 KB, application/pdf


    Accepted author manuscript

    The final publication is available at Springer via

King's Authors


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.

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