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Novel Clustering-Based Web Service Recommendation Framework

Author

Listed:
  • Priya Bhaskar Pandharbale

    (School of Computer Engineering, KIIT University (Deemed), Bhubaneswar, India)

  • Sachi Nandan Mohanty

    (Department of Computer Sc & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, India)

  • Alok Kumar Jagadev

    (School of Computer Engineering, KIIT University (Deemed), Bhubaneswar, India)

Abstract

Normally web services are classified originate in on the quality of service, wherever the term quality is not absolute and it is a relative term. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists some times. To address this research problem, the novel improved the Clustering-based web service recommendation method is proposed in this project. This approach is mainly dealing to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, we proposed a vary web service classify order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.

Suggested Citation

  • Priya Bhaskar Pandharbale & Sachi Nandan Mohanty & Alok Kumar Jagadev, 2021. "Novel Clustering-Based Web Service Recommendation Framework," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(5), pages 1-15, September.
    • Handle: RePEc:igg:jsda00:v:11:y:2021:i:5:p:1-15
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    References listed on IDEAS

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    1. Santosh Kumar Majhi, 2018. "An Efficient Feed Foreword Network Model with Sine Cosine Algorithm for Breast Cancer Classification," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 7(2), pages 1-14, April.
    2. Banage T. G. S. Kumara & Incheon Paik & Wuhui Chen & Keun Ho Ryu, 2014. "Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning," International Journal of Web Services Research (IJWSR), IGI Global, vol. 11(2), pages 24-45, April.
    3. Amos O. Omamo & Anthony J. Rodrigues & Wafula J. Muliaro, 2020. "A System Dynamics Model of Technology and Society: In the Context of a Developing Nation," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(2), pages 42-63, April.
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    Cited by:

    1. Priya Bhaskar Pandharbale & Sachi Nandan Mohanty & Alok Kumar Jagadev, 2022. "QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 13(6), pages 1-16, January.

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