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首页 > 过刊浏览>2022年第31卷第3期 >288-293. DOI:10.15888/j.cnki.csa.008356
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基于改进自编码器的在线课程推荐模型
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中国石油大学(华东)教学研究与改革项目(QT-202005)


Online Course Recommendation Model Based on Enhanced Auto-encoder
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    摘要:

    随着互联网技术的发展以及2020年新冠疫情的爆发, 越来越多的学生选择在线教育. 然而在线课程数量庞大, 往往无法及时找到合适的课程, 个性化智能推荐系统是解决这一问题的有效方案. 本文根据用户在线学习具有明显时序性的特点, 提出一种基于改进自编码器的在线课程推荐模型. 首先, 利用长短期记忆网络改进自编码器, 使得模型可以提取数据的时序性特征; 然后, 利用Softmax函数进行课程的推荐. 实验结果表明, 所提方法与协同过滤算法和基于传统自编码器的推荐模型相比, 具有更高的推荐准确率.

    Abstract:

    With the development of Internet technology and the outbreak of COVID-19 in 2020, more and more students have chosen online education. However, due to the large number of online courses, students are often unable to find suitable courses in time. A personalized intelligent recommendation system is an effective solution to this problem. Considering the obvious sequential characteristics of users for online learning, an online course recommendation model based on enhanced auto-encoders is proposed. First, the auto-encoder is enhanced with the long short-term memory network, so the model can extract the sequential characteristics of data. Then, the Softmax function is used to recommend online courses. Experimental results show that the proposed method has higher recommendation accuracy than the collaborative filtering algorithm and the recommendation model based on traditional auto-encoders.

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宋晓丽,贺龙威.基于改进自编码器的在线课程推荐模型.计算机系统应用,2022,31(3):288-293

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  • 收稿日期:2021-05-07
  • 最后修改日期:2021-06-08
  • 在线发布日期: 2022-01-24
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