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S-RAP: relevance-aware QoS prediction in web-services and user contexts

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Abstract

With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical techniques on historical data of user experiences of the available items/products. Research efforts provide several methods for web-service recommendation in which QoS-related attributes play primary role such as response-time, throughput, security, privacy and web-service-delivery. Derivable attributes including, user-trustworthiness and web-services reputation in contexts of users and web-services can also affect the QoS prediction. The proposed research focuses on a web-service recommendation model, S-RAP, for QoS prediction based on derivable attributes to predict QoS of a web-service that a user who has not invoked it before would experience. Services-Relevance attribute is proposed in this publication, which emphasizes on employing the historical data and extracting the degree of relevance in the users and web-services context to predict the QoS values for a user. The proposed system produces satisfactorily accurate rating predictions in the experiments evaluated by the Mean Absolute Error and Normalized Mean Absolute Error metrics. The results compared with state-of-the-art models show a relative improvement by 4.0%.

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Authors and Affiliations

  1. National University of Science and Technology (NUST), Islamabad, 44000, Pakistan

    Hafiz Syed Muhammad Muslim, Saddaf Rubab, Malik M. Khan, Naima Iltaf & Kashif Javed

  2. Department of Software Engineering and Computer Science, Faculty of Computing, Riphah International University, Islamabad, Pakistan

    Hafiz Syed Muhammad Muslim

  3. Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, UAE

    Saddaf Rubab

  4. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Ali Kashif Bashir

  5. School of Electrical Engineering and Computer Science, National University of Science and Technology (NUST), Islamabad, 44000, Pakistan

    Ali Kashif Bashir

Authors
  1. Hafiz Syed Muhammad Muslim
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  2. Saddaf Rubab
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  5. Ali Kashif Bashir
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Correspondence to Saddaf Rubab.

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Muslim, H.S.M., Rubab, S., Khan, M.M. et al. S-RAP: relevance-aware QoS prediction in web-services and user contexts. Knowl Inf Syst 64, 1997–2022 (2022). https://doi.org/10.1007/s10115-022-01699-0

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  • DOI: https://doi.org/10.1007/s10115-022-01699-0

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