基于协同过滤的Web服务动态社区发现算

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。

Dynamic community discovery algorithm of Web services based on collaborative filtering

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.