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Deep learning personalized recommendation-based construction method of hybrid blockchain model.

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This study introduces a novel personalized recommendation system (PRS) using deep learning and a hybrid blockchain model. The new system enhances security and efficiency, achieving lower delay and prediction errors.

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Area of Science:

  • Computer Science
  • Information Security

Background:

  • Personalized recommendation systems (PRS) face security challenges like information leakage.
  • Deep learning models offer potential for improved PRS performance.
  • Blockchain technology can enhance data security and integrity in distributed systems.

Purpose of the Study:

  • To develop a secure and efficient personalized recommendation system (PRS) using a hybrid blockchain model.
  • To improve the performance and security of deep learning-based PRS.
  • To address information leakage issues in existing PRS.

Main Methods:

  • Construction of a personalized recommendation system (PRS) integrating deep learning with a hybrid blockchain model.
  • Design of a Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph (PBDAG) consensus algorithm for public chains.
  • Development of a personalized recommendation model combining the PBDAG consensus algorithm with an optimized back propagation algorithm.

Main Results:

  • The proposed model demonstrates a lower average delay time compared to traditional consensus algorithms.
  • A stable data message delivery rate of 80% was achieved.
  • A data message leakage rate stabilized at approximately 10%.
  • System classification prediction error remained below 10%.

Conclusions:

  • The developed hybrid blockchain-based PRS offers enhanced network security and low delay performance.
  • The model facilitates more efficient and accurate information interaction.
  • This research provides an experimental foundation for information security in data-driven PRS across various fields.