Wandui Mao, Shi Yin, Lihua Wang, Changxin Shao, Y. Qu
{"title":"Expert-Application Grouping and Matching Algorithm","authors":"Wandui Mao, Shi Yin, Lihua Wang, Changxin Shao, Y. Qu","doi":"10.1109/WISM.2010.108","DOIUrl":null,"url":null,"abstract":"Objects grouping and matching simultaneously is an important means in grouping heterogeneous objects. In order to group and match the experts and applications in project-reviewing, the Expert-Application Algorithm is proposed in this paper, which is based on the co-clustering using Bipartite Spectral Graph Partitioning. In this algorithm, the number of the expert and application are configured at first. And then, the relationship between experts and applications are depicted as a bipartite graph model. The edge-weight is calculated by the relationship weight calculating formula witch is proposed in this paper. After decomposing the matrix witch is formed by the edge-weight, we get eigenvectors. At last, experts and applications are grouped and matched through mapping to the groups of the eigenvectors which are created by k-means grouping algorithm.","PeriodicalId":119569,"journal":{"name":"2010 International Conference on Web Information Systems and Mining","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Web Information Systems and Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISM.2010.108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Objects grouping and matching simultaneously is an important means in grouping heterogeneous objects. In order to group and match the experts and applications in project-reviewing, the Expert-Application Algorithm is proposed in this paper, which is based on the co-clustering using Bipartite Spectral Graph Partitioning. In this algorithm, the number of the expert and application are configured at first. And then, the relationship between experts and applications are depicted as a bipartite graph model. The edge-weight is calculated by the relationship weight calculating formula witch is proposed in this paper. After decomposing the matrix witch is formed by the edge-weight, we get eigenvectors. At last, experts and applications are grouped and matched through mapping to the groups of the eigenvectors which are created by k-means grouping algorithm.