结合时间相关度与课程搭配度的课程推荐方法  

Course Recommendation Method Combining Time Correlation Degree and Course Collocation Degree

作　　者：刘语珵 贺奇 董延华[1] 王晓宇 LIU Yu-cheng;HE Qi;DONG Yan-hua;WANG Xiao-yu(College of Mathematics and Computer Science,Jilin Normal University,Siping 136000,China;Jilin Seismological Bureau,Changchun 130000,China)

机构地区：[1]吉林师范大学数学与计算机学院,吉林四平136000 [2]吉林省地震局,吉林长春130000

出　　处：《计算机与现代化》2023年第12期53-58,93,共7页Computer and Modernization

基　　金：教育部高等学校科学研究发展中心项目(2022IT096)。

摘　　要：针对现有课程推荐系统存在过度依赖用户对课程的评分、未考虑用户兴趣随时间变化以及忽略了用户已学课程和推荐课程之间的搭配问题,提出一种基于时间相关度与课程搭配度的TIMR课程推荐模型。TIMR模型一方面采用课程观看进度代替课程评分,并将时间相关度函数应用于课程间的相似度计算;另一方面利用课程的共同被选频率,构建课程搭配度函数;然后将时间相关度与课程搭配度融合起来产生预测评分。为了验证TIMR模型的有效性,分别在TM数据集、CN数据集和MOOC数据集进行实验。实验表明,与现有推荐方法UserCF、ItemCF、LFM、PR、MPR、SMCR相比,TIMR在Precision、Recall、F1_score指标上均有明显提升,对于提高推荐质量具有明显的优势。In view of the problems in the existing course recommendation system,such as over-reliance on users'grades of courses,failure to consider the change of users'interests over time and neglect the collocation between the courses learned by users and the recommended courses,a TIMR course recommendation model based on the degree of time correlation and course collocation is proposed.On the one hand,TIMR model uses course viewing progress instead of course rating,and applies time correlation function to calculate the similarity between courses.On the other hand,the course collocation degree function is constructed by using the co-selected frequency of the course.Then,time correlation and course collocation are combined to produce predictive grades.In order to verify the validity of TIMR model,experiments are conducted on TM data set,CN data set and MOOC data set.Experiments show that compared with the existing recommendation methods UserCF,ItemCF,LFM,PR,MPR and SMCR,TIMR significantly improves the Precision,Recall and F1_score indexes,which has obvious advantages in improving the recommendation quality.

关 键 词：课程评分 观看进度 时间相关度 课程搭配度 课程推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]