Yu Xiaomo, Li Wenjing, Zhou Xiaomeng, Tang Ling, Sharma Rohit
Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, Guangxi, China.
Department of Logistics Management and Engineering, Nanning Normal University, Nanning, 530001, Guangxi, China.
Sci Rep. 2023 Oct 20;13(1):17915. doi: 10.1038/s41598-023-39564-x.
This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
本研究旨在探索混合区块链模型下基于深度学习的个性化推荐系统（PRS）的构建，以进一步提升PRS的性能。引入并进一步改进区块链技术，以解决PRS中诸如信息泄露等安全问题。为公有链设计了一种委托权益证明 - 拜占庭Algorand - 有向无环图共识算法，即PBDAG共识算法。最后，构建了基于混合区块链PBDAG共识算法并结合优化反向传播算法的个性化推荐模型。通过仿真，将该模型的性能与实用拜占庭容错、拜占庭容错、混合并行拜占庭容错、冗余拜占庭容错和委托拜占庭容错进行比较。结果表明，这里采用的模型算法平均延迟时间更低，数据消息传递率稳定在80%，数据消息泄漏率稳定在约10%，系统分类预测误差不超过10%。因此，所构建的模型不仅确保了低延迟性能，还具有较高的网络安全性能，能够实现更高效、准确的信息交互。该解决方案为各领域不同类型数据PRS的信息安全和发展趋势提供了实验依据。
