Paper Info
Title
Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm
Abstract
Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.
Year
DOI
Venue
2011
10.1016/j.eswa.2010.07.013
Expert Syst. Appl.
Keywords
Field
DocType
electronic customer relationship management,self-adaptive genetic algorithms,data mining,effective self-adaptive genetic algorithm,synthetic data set,catalog segmentation problem,catalog segmentation,e-crm,self-adaptive genetic algorithm,customer interaction,alternative problem formulation,clever feature,classical catalog segmentation algorithm,better performance,customer-oriented catalog,synthetic data
Customer relationship management,Data mining,Segmentation,Computer science,Local optimum,Self adaptive,Artificial intelligence,Database transaction,Synthetic data sets,Machine learning,Genetic algorithm
Journal
Volume
Issue
ISSN
38
1
Expert Systems With Applications
Citations 
PageRank 
References 
6
0.44
15
Authors
3
Name
Order
Citations
PageRank
Iraj Mahdavi138832.30
Mahyar Movahednejad260.44
Fereydoun Adbesh360.44