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A content-based recommendation algorithm for learning resources

机译：一种基于内容的学习资源推荐算法

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摘要

Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L (1)-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.
机译：自动多媒体学习资源推荐已成为一个越来越相关的问题：它允许学生发现符合自己口味的新学习资源，并使电子学习系统可以将学习资源定位到合适的学生。本文提出了一种基于卷积神经网络（CNN）的基于内容的推荐算法。 CNN可用于根据多媒体资源的文本信息预测潜在因素。要训​​练CNN，首先应解决其输入和输出。对于其输入，使用语言模型。对于其输出，我们提出了潜在因子模型，该模型通过L（1）-范数进行了正则化。此外，引入了分裂的Bregman迭代方法来求解模型。所提出的推荐算法的主要新颖之处在于，文本信息无需标记即可直接用于基于内容的推荐。在公共数据库上进行定量评估的实验结果表明，与传统方法相比，已有显着改进。另外，引入分裂Bregman迭代法求解模型可以大大提高训练效率。

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  • 来源
    《Multimedia Systems》 |2018年第2期|163-173|共11页
  • 作者

    Shu Jiangbo; Shen Xiaoxuan; Liu Hai; Yi Baolin; Zhang Zhaoli;

  • 作者单位

    Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China;

    Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China;

    Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China;

    Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China;

    Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Resources recommendation; Convolutional neural network; L-1 norm; Split Bregman iteration method;

    机译：资源推荐;卷积神经网络;L-1范数;Split Bregman迭代法;

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