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A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization.

Weihong Cai1,2, Xin Du3,4, Jianlong Xu5,6

  • 1Department of Computer Science, Shantou University, Shantou 515063, Guangdong, China. whcai@stu.edu.cn.

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|June 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a blockchain-based matrix factorization (BMF) method for accurate personalized quality of service (QoS) prediction. BMF effectively removes unreliable user data, significantly improving prediction accuracy over traditional methods.

Keywords:
QoS predictionblockchainquality of serviceweb services

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Area of Science:

  • Computer Science
  • Information Systems

Background:

  • Accurate personalized quality of service (QoS) prediction is crucial for developing high-quality service-oriented systems.
  • Existing prediction methods often struggle with untrustworthy QoS data from unreliable users, leading to inaccurate results.

Purpose of the Study:

  • To propose a novel personalized QoS prediction method that addresses the challenge of unreliable user data.
  • To enhance the accuracy and reliability of QoS predictions in service-oriented systems.

Main Methods:

  • A blockchain-based matrix factorization (BMF) approach is proposed, integrating distributed ledger technology and consensus mechanisms.
  • A user verification method using homomorphic hashing and Byzantine agreement is employed to identify and remove unreliable users.
  • Matrix factorization is utilized to improve prediction accuracy after data sanitization.

Main Results:

  • The proposed BMF method demonstrated significantly improved prediction accuracy compared to existing approaches.
  • Experimental evaluation on a real-world web services dataset confirmed the effectiveness of BMF.
  • The method proved more effective than traditional QoS prediction techniques.

Conclusions:

  • The blockchain-based matrix factorization (BMF) method offers a robust solution for personalized QoS prediction by handling untrustworthy data.
  • BMF enhances the reliability and accuracy of QoS predictions, benefiting the development of service-oriented systems.