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。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第8期2095-2099,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(71072077
71172043)
国家科技支撑计划项目(2011BAH16B02)
教育部留学回国人员科研启动基金资助项目(20101561)
中央高校基本科研业务费专项资金资助项目(2012YB20)
关键词
WEB服务资源
协同过滤
社会网络
重叠社区
社区挖掘
节点相似性
Web service resource
collaborative filtering
social network
overlapping community
community mining
node similarity
