Abstract
The latest developments in edge computing have paved the way for more efficient data processing especially for simple tasks and lightweight models on the edge of the network, sinking network functions from cloud to edge of the network closer to users. For the reform of English teaching mode, this is also an opportunity to integrate information technology, providing new ideas and new methods for the optimization of English teaching. It improves the efficiency of English reading teaching, stimulates the interest of English learning, enhances students’ autonomous learning ability, and creates favorable conditions for students’ learning and development. This paper designs a MEC-based GNN (GCN-GAN) user preference prediction recommendation model, which can recommend high-quality video or picture text content to the local MEC server based on user browsing history and user preferences. In the experiment, the LFU-LRU joint cache placement strategy used in this article has a cache hit rate of up to 99%. Comparing the GCN-GAN model with other traditional graph neural network models, it performs caching experiments on the Douban English book data and Douban video data sets. The GCN-GAN model has a higher score on the cache task, and the highest speculation accuracy value F1 can reach 86.7.
Highlights
In recent years, applications based on deep learning have brought great improvement to people’s lives
This section of the experiment is mainly to test the performance of the English teaching content recommendation platform based on the mobile edge computing (MEC)-based Graph Neural Network (GNN) (GCN-graph attention mechanism (GAN)) neural network proposed in this paper
The collaborative caching mechanism of graph neural network based on mobile edge computing in this paper is for the reform of English teaching mode, so the content of cache prediction is the English learning-related content browsed by teachers or students in the wireless network domain
Summary
Applications based on deep learning have brought great improvement to people’s lives. In view of the disadvantages of traditional English classroom teaching, new technology can provide new ideas and methods for the reform of English teaching mode. With the support of information technology, English teaching mode reform is carried out to get rid of the shortcomings of traditional English teaching mode, so as to expand students’ knowledge and cultivate students’ reading ability. The experimental data set is divided into three categories. The first category is the miniImagNet data set, which is used to test the content classification performance of the platform. This data set is an excerpt from the ImageNet data set and is a small classification data set. The second and third types of data are Douban English book data and Douban video data, respectively, used to test the prediction accuracy of user preference transfer
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