张鹏程

摘  要： 傳统的远程教育课程推荐方法因数据稀疏问题，造成其主题集中性较差，为此设计基于LDA用户兴趣模型的远程教育课程推荐方法。通过远程教育课程外在属性包容度和内在属性质量值，计算远程教育课程的重要度，并以重要度为依据，利用LDA用户兴趣模型判断用户对主题的偏好度，确定主题与远程教育课程的相似度系数，获得用户对远程教育课程的兴趣度，以此为基础完成远程教育课程的推荐。实验结果表明：使用基于LDA模型的推荐方法向用户推荐的课程有50%以上都是用户需求的课程，而传统的推荐方法只有不到20%，两者相比，基于LDA模型的推荐方法的主题集中性更强，更适合应用在远程教育课程推荐中。

关键词： 远程教育; 课程推荐; LAD用户兴趣模型; 主题确定; 重要度计算; 偏好度判断

中图分类号： TN911?34; TP301                    文献标识码： A                     文章编号： 1004?373X（2020）03?0173?04

Research on distance education course recommendation method

based on LDA user interest model

ZHANG Pengcheng

（Henan Radio & Television University， Zhengzhou 450008， China）

Abstract： The traditional distance education course recommendation method is poor in topic concentration due to data sparsity. Therefore， a distance education course recommendation method based on LDA （latent Dirichlet allocation） user interest model is designed. The importance degree of distance education courses is calculated according to the inclusiveness of external attributes and the quality value of internal attributes of distance education courses. The users′ preference to the subject is determined by LDA user interest model on the basis of importance degree to determine the similarity coefficients between subjects and distance education courses and obtain users′ interestingness for distance education courses. The distance education courses are recommended according to the obtained users′ interestingness. The experimental results show that more than 50% of the courses recommended to users by the LDA model based recommendation method are the courses required by users， while the correctness of the courses recommended to users by the traditional recommendation method is less than 20%. In comparison with the traditional recommendation method， the LDA model based recommendation method has better topic concentration and is more suitable for distance education course recommendation.

Keywords： distance education; course recommendation; LAD user interest model; subject determination; importance degree calculation; preference degree judgment