基于数据挖掘算法的思政教学资源推荐方法设计

Design of Ideological and Political Teaching Resource Recommendation Method Based on Data Mining Algorithm

为了提高当前教学资源的推荐准确性,提出基于数据挖掘算法的思政教学资源推荐方法。运用数据挖掘算法获取线上思政教学资源应用背景状态和线上行为,计算奖励值参数构建思政教学资源模型。在学习库中添加标签属性表示特征,计算标签的思政教学资源相似度,获取新资源的近邻思政教学资源。利用学生在目标领域中的相似度得到学生最终的用户兴趣度矩阵,运用k-means聚类算法对资源进行聚类,计算学生距离中心点的距离,完成思政教学资源推荐。实验结果表明,随着参数的变化,实验组精准程度在90%以上,召回率达到90%,能够达到较好的推荐效果。

In order to improve the recommendation accuracy of current teaching resources,a method of recommending ideological and political teaching resources based on data mining algorithm is proposed.The data mining algorithm is used to obtain the application background state and online behavior of ideological and political teaching resources in mathematics online,and the reward value parameters are calculated to construct the ideological and political teaching resources model.Add tag attributes to the learning library to represent features,calculate the similarity of tag ideological and political teaching resources,and obtain the neighboring ideological and political teaching resources of new resources.The final user interest matrix of students is obtained by using the similarity of students in the target field,and the resources are clustered by using k-means clustering algorithm,and the distance between students and the center point is calculated to complete the recommendation of ideological and political teaching resources.The experimental results show that with the change of parameters,the accuracy of the experimental group is more than 90%,and the recall result is 90%,which can achieve a better recommended effect.