1 |
Pham, M. C., Y. Cao, R. Klamma, and M. Jarke, "A clustering approach for collaborative filtering recommendation using social network analysis," J. UCS, Vo.17, No.4(2011), 583-604.
|
2 |
Tsai, C. F. and Y. H. Lu, "Customer churn prediction by hybrid neural networks," Expert Systems with Applications, Vol.36, No.10 (2009), 12547-12553.
DOI
|
3 |
Wen, J. and W. Zhou, "An improved item-based collaborative filtering algorithm based on clustering method," Journal of Computational Information Systems, Vol.8, No.2(2012), 571-578.
|
4 |
Xie, Y. and X. Li, "Churn prediction with linear discriminant boosting algorithm," Proceedings of IEEE International Conference on Machine Learning and Cybernetics, (2008), 228-233.
|
5 |
Xie, Y., X. Li, E. W. T. Ngai, and W. Ying, "Customer churn prediction using improved balanced random forests," Expert Systems with Applications, Vol.36, No.3(2009), 5445-5449.
DOI
|
6 |
Athanassopoulos, A. D., "Customer satisfaction cues to support market segmentation and explain switching behavior," Journal of business research, Vol.47, No.3(2000), 191-207.
DOI
|
7 |
Chang, M. S. and H. J. Kim, "A Customer Segmentation Scheme Base on Big Data in a Bank," Journal of Digital Contents Society, Vol.19, No.1(2018), 85-91.
DOI
|
8 |
Chang, N. S., "Improving the effectiveness of customer classification models: a pre-segmentation approach," Information Systems Review, Vol.7, No.2(2005), 23-40.
|
9 |
Cho, Y. and J. Bang, "Applying centrality analysis to solve the cold-start and sparsity problems in collaborative filtering," Journal of Intelligence and Information Systems, Vol.17, No.3(2011), 99-114.
DOI
|
10 |
Cho, Y. S., M. H. Huh, and K. H. Ryu, "Implementation of Personalized Recommendation System using RFM method in Mobile Internet Environment," Journal of The Korea Society of Computer and Information, Vol.13, No.2(2008), 41-50.
|
11 |
De Bock, K. W. and D. Van den Poel, "Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models," Expert Systems with Applications, Vol.39, No.8(2012), 6816-6826.
DOI
|
12 |
Han, S. L. and A. Maeng, "Effects of Switching Barrier of Mobile Telecommunication Service on Customer Retention And Churn-out," Proceedings of the Korea Distribution Association Conference, (2004), 191-197.
|
13 |
Hong, T. and B. Suh, "Data Mining for Personalization Model Using Customer Belief under the Internet Banking Environment," The Journal of Internet Electronic Commerce Resarch, Vol.4, No.2(2004), 101-115.
|
14 |
Hung, C. and C. F. Tsai, "Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand," Expert systems with applications, Vol.34, No.1(2008), 780-787.
DOI
|
15 |
Hung, S. Y., D. C. Yen, and H. Y. Wang, "Applying data mining to telecom churn management," Expert Systems with Applications, Vol.31, No.3(2006), 515-524.
DOI
|
16 |
Joe, D. Y and K. Nam, "SKU recommender system for retail stores that carry identical brands using collaborative filtering and hybrid filtering," Journal of Intelligence and Information Systems, Vol.23, No.4(2017), 77-110.
DOI
|
17 |
Kawale, J., A. Pal, and J. Srivastava, "Churn prediction in MMORPGs: A social influence based approach," 2009 International Conference on Computational Science and Engineering, Vol.4, (2009), 423-428
|
18 |
Kim, H. S. and D. Y. Shin, "Developing a customer defection model based on relative defection criteria for non-contractual businesses," Korean Journal of Marketing, Vol.27, No.3 (2012), 117-144.
|
19 |
Kim, C. N., N. S. Chang, and J. W. Kim, "A Study on the Analysis of Comparison of Churn Prediction Models in Mobile Telecommunication Services," Asia Pacific Journal of Information Systems, Vol.12, No.1 (2002), 139-158.
|
20 |
Kim, D. H. and K. K. Ahn, "A Study on the Customer Segmentation Using Machine Learning," The Society of Convergence Knowledge Transactions, Vol.6, No.2(2018), 115-120.
|
21 |
Lee, J. S. and J. C. Lee, "Customer Churn Prediction of Automobile Insurance by Multiple Models," Journal of Intelligence and Information Systems, Vol.12, No.2(2006), 167-183.
|
22 |
Kim, H. S., Y. Bak, and J. Lee, "A Personalized Recommendation System Using Machine Learning for Performing Arts Genre," Information Systems Review, Vol.21, No.4(2019), 31-45.
DOI
|
23 |
Kim, H.S. and Y.G. Kim, "CRM Strategy: Principles and Applecations," Young Publication, 2015.
|
24 |
Kraljevic, G. and S. Gotovac, "Modeling data mining applications for prediction of prepaid churn in telecommunication services," Automatika, Vol.51, No.3(2010), 275-283.
DOI
|
25 |
Lee, K. C., S. J. Kwon, and K. S. Shin, "Analysis of Defection Customer Using Customer Segmentation on Bank," Journal of Intelligence and Information Systems, Vol.7, No.1(2001), 177-196.
|
26 |
Nath, S. V. and R. S. Behara, "Customer churn analysis in the wireless industry: A data mining approach," Proceedings-annual meeting of the decision sciences institute, (2003), Vol. 561, 505-510.
|
27 |
Nie, G., G. Wang, P. Zhang, Y. Tian, and Y. Shi, "Finding the hidden pattern of credit card holder's churn: A case of china," Proceedings of Springer International Conference on Computational Science, (2009), 561-569.
|
28 |
Oh, S., E. Lee, J. Woo, and H. K. Kim, "Constructing and Evaluating a Churn Prediction Model using Classification of User Types in MMORPG," KIISE Transactions on Computing Practices, Vol.24, No.5(2018), 220-226.
DOI
|
29 |
Park, S. H. and H. S. Kim, "Design of a Diversity-Based Recommender System for Providing Anti-Churning Rules," Korea Intelligent Information System Society, Vol.11, No.3 (2012), 101-112.
|