基于Canopy和共享最近邻的服务推荐算法  

Service Recommendation Algorithm Based on Canopy and Shared Nearest Neighbor

作　　者：邵欣欣[1] SHAO Xin-xin(Dalian Neusoft University of Information,Dalian,Liaoning 116023,China)

机构地区：[1]大连东软信息学院,辽宁大连116023

出　　处：《计算机科学》2020年第S02期479-481,503,共4页Computer Science

基　　金：辽宁省自然科学基金(2019-ZD-0354)。

摘　　要：为辅助银行机构进行精准的服务推荐,提出了基于改进的Canopy和共享最近邻相似度的聚类算法。基于该算法对用户进行细分,针对用户群特点进行精准服务推荐。该算法首先采用最大值和最小值对Canopy算法进行改进,并应用该算法得出初始聚类结果,然后利用共享最近邻相似度算法对聚类结果中的交叉部分数据进行归类,最终得出用户聚类数据。该算法在某银行对真实客户数据进行应用,选择基于客户的贡献度、忠诚度和活跃度3个指标进行聚类,结果表明,该算法提高了客户细分的质量和聚类的效率,聚类结果对于客户的消费数据刻画非常准确,能够为银行的精准服务推荐提供数据支持。In order to improve the accuracy of banking institution service recommendation,a clustering algorithm based on the improved Canopy and shared nearest neighbor similarity is proposed.Based on this algorithm,users are subdivided and accurate service recommendation is made according to the characteristics of user groups.First,the improved Canopy algorithm is used to obtain the initial clustering results.Then the shared nearest neighbor similarity algorithm is used to classify the intersecting data in the clustering results.Finally,the user clustering data are obtained.The algorithm is applied to the real customer data of a bank.Three indexes of customer contribution,loyalty and activity are selected for clustering.The results show that the algorithm improves the quality of customer segmentation and the efficiency of clustering.The result of clustering is very accurate in describing the consumption data of customers.Clustering results can provide data support for accurate service recommendation of banks.

关 键 词：客户聚类 服务推荐 Canopy算法 共享最近邻相似度 聚类指标 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]