基于TWE-NMF主题模型的Mashup服务聚类方法

TWE-NMF Topic Model-based Approach for Mashup Service Clustering

随着互联网和面向服务技术的发展,一种新型的Web应用——Mashup服务,开始在互联网上流行并快速增长.如何在众多Mashup服务中找到高质量的服务,已经成为一个大家关注的热点问题.寻找功能相似的服务并进行聚类,能有效提升服务发现的精度与效率.目前国内外主流方法为挖掘Mashup服务中隐含的功能信息,进一步采用特定聚类算法如K-means等进行聚类.然而Mashup服务文档通常为短文本,基于传统的挖掘算法如LDA无法有效处理短文本,导致聚类效果并不理想.针对这一问题,提出一种基于非负矩阵分解的TWE-NMF(nonnegative matrix factorization combining tags and word embedding)模型对Mashup服务进行主题建模.所提方法首先对Mashup服务规范化处理,其次采用一种基于改进的Gibbs采样的狄利克雷过程混合模型,自动估算主题的数量,随后将词嵌入和服务标签等信息与非负矩阵分解相结合,求解Mashup服务主题特征,并通过谱聚类算法将服务聚类.最后,对所提方法的性能进行了综合评价,实验结果表明,与现有的服务聚类方法相比,所提方法在准确率、召回率、F-measure、纯度和熵等评价指标方面都有显著提高.

With the development of the Internet and service-oriented technology,a new type of Web application—Mashup service,began to become popular on the Internet and grow rapidly.How to find high-quality services among large number of Mashup services has become a focus of attention.It has been shown that finding and clustering services with similar functions can effectively improve the accuracy and efficiency of service discovery.At present,current methods mainly focus on mining the hidden functional information in the Mashup service,and use specific clustering algorithms such as K-means for clustering.However,Mashup service documents are usually short texts.Traditional mining algorithms such as LDA are difficult to represent short texts and find satisfied clustering effects from them.In order to solve this problem,this study proposes a non-negative matrix factorization combining tags and word embedding(TWE-NMF)model to discover topics for the Mashup services.This method firstly normalizes the Mashup service,then uses a Dirichlet process multinomial mixture model based on improved Gibbs sampling to automatically estimate the number of topics.Next,it combines the word embedding and service tag information with non-negative matrix factorization to calculate Mashup topic features.Moreover,a spectral clustering algorithm is used to perform Mashup service clustering.Finally,the performance of the method is comprehensively evaluated.Compared with the existing service clustering method,the experimental results show that the proposed method has a significant improvement in the evaluation indicators such as precision,recall,F-measure,purity,and entropy.