《计算机应用研究》|Application Research of Computers

基于MLP改进型深度神经网络学习资源推荐算法

Improved learning resource recommendation algorithm of deep neural network based on MLP

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作者 樊海玮,史双,张博敏,张艳萍,蔺琪,孙欢
机构 长安大学 信息工程学院,西安 710064
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文章编号 1001-3695(2020)09-013-2629-05
DOI 10.19734/j.issn.1001-3695.2019.04.0109
摘要 针对在线学习过程中出现的知识过载及传统推荐算法中存在的数据稀疏和冷启动问题,提出了一种基于多层感知机(MLP)的改进型深度神经网络学习资源推荐算法。该算法利用多层感知机对非线性数据处理的优势,将学习者特征和学习资源特征进行向量相乘的预测方式转换为输入多层感知机的方式,改进了DN-CBR神经网络推荐模型。为验证模型的有效性,以爱课程在线学习平台数据为样本构建数据集,通过对比实验表明,在该数据集上,改进后模型相较于DN-CBR模型在归一化折损累积增益和命中率指标上分别提升了1.2%和3%,有效地提高了模型的推荐性能。
关键词 学习资源推荐; 深度学习; 卷积神经网络; word2vec; 多层感知机
基金项目 中央高校教育教学改革专项资助项目(300103190639,300103292405,300103292403,300103190605)
本文URL http://www.arocmag.com/article/01-2020-09-013.html
英文标题 Improved learning resource recommendation algorithm of deep neural network based on MLP
作者英文名 Fan Haiwei, Shi Shuang, Zhang Bomin, Zhang Yanping, Lin Qi, Sun Huan
机构英文名 Institute of Information Engineering,Chang'an University,Xi'an 710064,China
英文摘要 Aiming at the knowledge overload in the process of online learning and the problems of data sparsity and cold start in the traditional recommendation algorithm, this paper proposed an improved learning resource recommendation algorithm based on MLP. The algorithm used the advantage of MLP in nonlinear data processing, converted the prediction mode of vector multiplication of learner characteristics and learning resource characteristics into the input mode of MLP, and improved the DN-CBR neural network recommendation model. In order to verify the effectiveness of the model, this paper built a dataset with the online learning platform data of iCourse as the sample. Compared with DN-CBR model, the improved model improves the normalized cumulative loss gain and hit rate by 1.2% and 3% respectively on the dataset, which effectively improves the recommendation performance of the model.
英文关键词 recommendation of learning resources; deep learning; convolutional neural network; word2vec; multi-layer perceptron(MLP)
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收稿日期 2019/4/30
修回日期 2019/7/2
页码 2629-2633
中图分类号 TP183
文献标志码 A