数据稀疏情况下的关联规则扩展与应用

Extension and Application of Association Rules in Data Sparse Cases

在网络学习平台中,如何有效推荐学习资源具有重要意义。为进一步提高在数据稀疏情况下的关联规则推荐效率,从相似性角度出发,引入学习资源文本信息,通过构建学习资源相似度矩阵,提出一个基于资源相似度的关联规则扩展方法,从而在历史数据稀疏的情况下生成关联规则推荐。实践结果表明,基于同一数据对象进行研究,通过扩展关联规则方法可以提高推荐的有效性和实用性。

In the network learning platform,how to effectively recommend learning resources is of great significance.To further improve the efficiency of association rule recommendation in the case of sparse data,from the perspective of similarity,this paper introduces the text information of learning resources,and constructs a similarity matrix of learning resources to propose an association rule expansion method based on resource similarity.Correlation rule recommendation is generated when the data is sparse.The practical results show that the effectiveness and practicability of recommendation can be improved by extending association rules based on the same data.