Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (08): 2095-2099.DOI: 10.11772/j.issn.1001-9081.2013.08.2095

• Network and communications • Previous Articles     Next Articles

Dynamic community discovery algorithm of Web services based on collaborative filtering

Zhong WU1,2,3,Gui-hua NIE3,CHEN Dong-lin3,ZHANG Peilu3   

  1. 1.
    2. Department of Economic Management, Wuhan University of Technology Huaxia College, Wuhan Hubei 430223, China
    3. School of Economics, Wuhan University of Technology, Wuhan Hubei 430070, China
  • Received:2013-02-25 Revised:2013-03-25 Online:2013-09-11 Published:2013-08-01
  • Contact: Zhong WU

基于协同过滤的Web服务动态社区发现算

吴钟1,2,3,聂规划2,陈冬林2,章佩璐2   

  1. 1.
    2. 武汉理工大学 经济学院,武汉 430070
    3. 武汉理工大学华夏学院 经济与管理系,武汉 430223
  • 通讯作者: 吴钟
  • 作者简介:吴钟(1982-),男,湖北武汉人,讲师,博士研究生,主要研究方向:Web服务、信息经济、服务管理;
    聂规划(1958-),男,河南周口人,教授,博士,主要研究方向:商务智能、知识管理、知识工程;
    陈冬林(1970-),男,湖北孝感人,教授,博士,主要研究方向:云计算、服务管理;
    章佩璐(1986-),女,浙江宁波人,硕士,主要研究方向:语义网、服务管理、商务智能。
  • 基金资助:

    国家自然科学基金资助项目;国家科技支撑计划项目;教育部留学回国人员科研启动基金资助项目;中央高校基本科研业务费专项资金资助项目

Abstract: To cope with the low accuracy of the mining results in the existing community discovery algorithms and the low quality of intelligent recommendation in the Web services resource, on the basis of the conventional collaborative filtering algorithms, a dynamic community discovery algorithm was proposed based on the nodes' similarity. Firstly, the central node that had the most connected nodes was regarded as the initial network community, and the community contribution degree was taken as the metric to continuously form a plurality of global saturated contribution degree communities. Then, an overlapping calculation was used to merge the communities of high similarity. Finally, the calculated results were arranged in descending order to form neighboring user sets for obtaining community recommendation object by calculating the dynamic similarity between target user and other users in the community. The experimental results show that the user social network community classification by the proposed community discovery algorithms is consistent with the real community classification results. The proposed algorithm can improve the accuracy of the community mining and helps to achieve high-quality community recommendation.

Key words: web service resources, collaborative filtering, social network, overlapping community, nodes&rsquo, similarity

摘要: 针对现有社区发现算法挖掘结果精确度不高以及Web服务资源智能推荐质量较低的问题,在传统协同过滤算法的基础上,提出了基于节点相似性的动态社区发现算法。首先以连接节点最多的中心节点为起始网络社区,以社区贡献度为衡量指标不断形成多个全局贡献度饱和的社区;再使用重叠度计算将相似度高的社区进行合并,最后通过计算目标用户与社区中其他用户之间的动态相似度,将计算结果降序排列后构成邻近用户集,获得社区化推荐对象。实验结果表明,提出的社区发现算法对用户社会网络的社区分类与实际社区分类结果吻合,提高了社区挖掘的精确度,有助于实现高质量的社区化推荐。

关键词: Web服务资源, 协同过滤, 社会网络, 重叠社区, 节点相似性

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