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Enhanced Time-Aware Collaborative Filtering for QoS Web Service Prediction

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Service-Oriented and Cloud Computing (ESOCC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14183))

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Abstract

Predicting Quality of Service (QoS) is an essential task in Service Oriented Computing (SOC). In service selection, choosing the right services is a crucial step to achieve high system stability and user satisfaction. Considerable research has been conducted in the last decade to develop accurate prediction methods. Among these are the time-aware Collaborative Filtering (CF) methods, which utilize the QoS values recorded across multiple time periods (slices). However, they suffer from low accuracy due to adopting inaccurate measures, such as averaging old collected QoS or averaging user (or service) similarity values. In this paper, we propose a time-aware method (ETACF) that uses an exponential time-decay function for quantifying the effectiveness of time slices according to their temporal recency. Experiments were conducted in order to evaluate the accuracy of the proposed method. Results show that the developed method achieves a significant improvement in prediction accuracy (decreased NMAE by 9.8%) when compared with the state-of-art methods.

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Authors and Affiliations

  1. Department of Computer Science, Birzeit University, Birzeit, Palestine

    Ezdehar Jawabreh & Adel Taweel

  2. Palestine Polytechnic University, Hebron, Palestine

    Ezdehar Jawabreh

Authors
  1. Ezdehar Jawabreh
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  2. Adel Taweel
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Corresponding author

Correspondence to Ezdehar Jawabreh .

Editor information

Editors and Affiliations

  1. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  2. RWTH Aachen University, Aachen, Germany

    Florian Rademacher

  3. University of Pisa, Pisa, Italy

    Jacopo Soldani

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Jawabreh, E., Taweel, A. (2023). Enhanced Time-Aware Collaborative Filtering for QoS Web Service Prediction. In: Papadopoulos, G.A., Rademacher, F., Soldani, J. (eds) Service-Oriented and Cloud Computing. ESOCC 2023. Lecture Notes in Computer Science, vol 14183. Springer, Cham. https://doi.org/10.1007/978-3-031-46235-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-46235-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46234-4

  • Online ISBN: 978-3-031-46235-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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