基于深度学习的学生个性化在线学习平台课程推荐算法

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhengmeng Xu, Hai Lin, Meiping Wu
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引用次数: 0

摘要

本文主要研究MOOC等在线学习平台中学习资源课程推荐算法的内容,主要引入集成课程相关性的自动编码器神经网络,实现个性化课程推荐模型。首先介绍了如何在自编码器神经网络中嵌入课程相关解码器。其次,引入了所提出的置信度矩阵方法来区分已学习课程对未学习课程的推荐效果,并介绍了模型的训练过程。然后介绍了实验的设计内容,包括模型结构、对比实验、参数设置和评价指标。最后,从横向和纵向两个方面对实验结果进行了详细分析。希望本研究能为基于深度学习技术和大数据分析的学习资源个性化推荐提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning
This paper mainly studies the content of the recommendation algorithm of learning resource courses in online learning platforms such as MOOC and mainly introduces the automatic encoder neural network that integrates course relevance to realize the personalized course recommendation model. The authors first introduce how to embed a course relevance decoder in an autoencoder neural network. Secondly, the proposed confidence matrix method is introduced to distinguish the recommendation effect of the learned to the unlearned courses, and the training process of the model is introduced. Then, the design content of the experiment is introduced, including the model structure, comparative experiments, parameter settings, and evaluation indicators. Finally, the experimental results are analyzed in detail from the horizontal and vertical aspects. It is hoped that this research can provide a reference for personalized recommendation of learning resources based on deep learning technology and big data analysis.
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
24
期刊介绍: Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.
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