基于微博的用户相似度计算研究

Computing Research of User Similarity Based on Micro-blog

针对传统的相似度计算方法和评价标准在准确、高效地度量微博用户相似关系时不理想的缺陷,提出一种新的微博用户相似度的计算方法。该方法针对不同的属性数据结构采用不同的计算方式,并根据属性统计信息对各个属性赋值,利用层次分析法确定各属性权值,最后构建综合相似度计算模型。实验结果表明,相对于传统的相似度计算方法,所提计算方法衡量用户相似的准确度提高了22.6%,召回率提高了12.7%,F1度量值提高了29.5%。

For traditional similarity calculation methods and evaluation criteria have defects that accurately and efficiently measuring micro-blog users similar relationship is undesirable,a new method to calculate micro-blog user similarity was proposed.For different attribute data structure,different calculation methods are used,and based on the assignment of property statistics for each property,the method uses AHP to determine the weight of each attribute,finally building an integrated similarity calculation model.Experimental results show that the improved method of calculation to measure user similarity precision increases by 22.6%,recall rate increases by 12.7%,and F1 metric improves 29.5%.