基于Canopy和共享最近邻的服务推荐算法

doi:10.11896/jsjkx.200200031

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 479-481.doi: 10.11896/jsjkx.200200031

• Big Data & Data Science • Previous Articles Next Articles

Service Recommendation Algorithm Based on Canopy and Shared Nearest Neighbor

SHAO Xin-xin

Dalian Neusoft University of Information,Dalian,Liaoning 116023,China

Online:2020-11-15 Published:2020-11-17
About author:SHAO Xin-xin,born in 1980,postgra-duate,assistant professor.Her main research interests include computer software and theory,and big data.
Supported by:
This work was supported by the Natural Science Foundation of Liaoning Province,China(2019-ZD-0354).

Abstract

Abstract: 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.

Key words: Canopy algorithm, Clustering index, Customer clustering, Service recommendation, Shared nearest neighbor similarity

CLC Number:

TP391.9

SHAO Xin-xin. Service Recommendation Algorithm Based on Canopy and Shared Nearest Neighbor[J].Computer Science, 2020, 47(11A): 479-481.

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