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Network education recommendation and teaching resource sharing based on improved neural network

机译：基于改进神经网络的网络教育推荐与教学资源共享

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摘要

Intelligent network teaching system provides learners with abundant teaching resources and convenient, excellent and efficient learning environment. However, network teaching resources are widely distributed and difficult to centralize. Resource sharing has become a key problem to be solved in the network environment. The current research on online education resource recommendation mainly focuses on offline education, and there are few studies on online education resources. Based on this, this study studies the link prediction methods in online education and establishes appropriate models for online education. In the research, through improved analysis of traditional algorithms, an improved neural network path sorting algorithm based on path sorting method is proposed. At the same time, we use the path sorting algorithm based on random walk model and neural network-path sorting algorithm to realize the link prediction problem in the online learning knowledge base. In addition, the performance analysis of the algorithm is carried out by contrast method, and the performance comparison analysis is carried out by combining various common traditional recommendation algorithms with the research algorithm of this study.
机译：智能网络教学系统为学习者提供丰富的教学资源，方便，高效的学习环境。然而，网络教学资源广泛分布，难以集中。资源共享已成为网络环境中解决的关键问题。目前关于在线教育资源建议的研究主要侧重于开线教育，少数关于在线教育资源的研究。基于此，本研究研究了在线教育中的链接预测方法，并为在线教育建立了适当的模型。在该研究中，提出了一种基于路径排序方法的改进的传统算法的分析。同时，我们使用基于随机步道模型的路径排序算法和神经网络路径排序算法来实现在线学习知识库中的链路预测问题。此外，通过对比法进行算法的性能分析，通过将各种常见的传统推荐算法与本研究的研究算法相结合来执行性能比较分析。

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