Intelligent Approach for Attracting Churning Customers in Banking Industry Based on Collaborative Filtering

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Industrial Engineering and Management Systems, Amir kabir University of Technology, Tehran, Iran

3 Iranian Research Institute for Information Science and Technology (IRANDOC), Tehran, Iran

Abstract

During the last years, increased competition among banks has caused many developments in banking experiences and technology, while leading to even more churning customers due to their desire of having the best services. Therefore, it is an extremely significant issue for the banks to identify churning customers and attract them to the banking system again. In order to tackle this issue, this paper proposes a novel personalized collaborating filtering recommendation approach joint with the user clustering technology. In the proposed approach, first the loyal customers are clustered by means of hybrid algorithm based on Particle Swarm Optimization (PSO) and K-means. The clusters of loyal customers are then used to identify the features of the churning customers. Finally, the list of appropriate banking services are recommended for the churning customers based on a collaborative filtering recommendation system. The recommendation system uses the information of loyal customers to offer appropriate services for the churning customers. The proposed intelligent approach was successfully applied to return the churning customers of an Iranian bank. 

Keywords

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References

-          Abdollahpouri, H., & Abdollahpouri, A. (2013). An approach for personalization of banking services in multi-channel environment using memory-based collaborative filtering. Proceeding of 5th  Conference on Information and Knowledge Technology (IKT), pp. 208-213.
-          Ahmadi, A., Karray, F., & Kamel, M.S. (2010). Flocking based approach for data clustering. Natural Computing, 9(3), 297-321.
-          Aimée, B., Baesens, B., & Claeskens, G. (2016). Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. Journal of the Operational Research Society. DOI 10.1057/jors.2016.8.
-          Bi, W.,  Cai, M., Liu, M., &  Li, G. (2016). A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics, 12(3), 1270-1281, 2016.
-          Bridge, D., & Kelleher, J. (2002). Experiments in sparsity reduction: Using clustering in collaborative recommenders. Artificial Intelligence and Cognitive Science, Springer Berlin Heidelberg, pp.144-149.
-          Chee, S.H.S. , Han, J., & Wang, K. (2001). Rectree: An efficient collaborative filtering method. Data Warehousing and Knowledge Discovery, Springer Berlin Heidelberg, pp. 141-151.
-          Conner, M.O., & Herlocker, J. (1999). Clustering Items for Collaborative Filtering. Proceedings of the ACM SIGIR Workshop on Recommender Systems, UC Berkeley, Vol. 128.
-          Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science,  vol 1, pp. 39-43.
-          Fathian, M., Hoseinpoor, Y., & Minaei-Bidgoli, B. (2016).  Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes, 45(5), 732-743.
-          Honda, K., Sugiura, N., Ichihashi, H., & Araki, S. (2001). Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering. Web Intelligence: Research and Development, Springer Berlin Heidelberg, pp. 394-402.
-          Kelleher, J., & Bridge, D. (2003). Rectree centroid: An accurate, scalable collaborative recommender”. Proceeding of AICS.
-          Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceeding of  IEEE international conference on Neural networks, New Jersey, USA.
-          Liao, H., Chen, K., Liu, D., & Chiu, Y. (2015). Customer Churn Prediction in Virtual Worlds. In Advanced Applied Informatics (IIAI-AAI), 4th International Congress on, pp. 115-120.
-          Lu, N., Lin, H., Lu,, & Zhang,G. (2012). A Customer Churn Prediction Model in Telecom Industry Using Boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659-1665.
-          Mamunur, R.A., George, R. , & Ried, K.J. (2005). Influence in Ratings Based Recommender Systems: an Algorithm-independent Approach. Processing of SIAM International Conference on Data Mining.
-          Mamunur, R.A., Lam, S.K., Karypis, G., & Riedl, J. (2006). A Highly Scalable Hybrid Model- & Memory-Based CF Algorithm. Proceeding of WebKDD.
-          Miller, B., Konstan, J., & Riedl, J. (2004). PocketLens: Toward a Personal Recommender System. ACM Transactions on Information Systems (TOIS), 22(3), 437-476.
-          Renaud-Deputter, S., Xiong, T., & Wang, S. (2013). Combining collaborative filtering and clustering for implicit recommender system”, Proceeding of IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 748-755.
-          Rui, X. , & Wunsch, D. (2005). Survey of Clustering Algorithms. IEEE Transaction on Neural Networks, 16(3), 645-678.
-          Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. Proceedings of the Fifth International Conference on Computer and Information Technology, Vol. 1.
-          Shen, H., Jin, L., Zhu, Y., & Zhu, Z. (2010). Hybridization of particle swarm optimization with the K-Means algorithm for clustering analysis. Proceeding of IEEE Fifth International Conference, pp. 531-535.
-          Shishehchi, S., Banihashem, S.Y., Zin, N.A.M., & Noah, S.M. (2011). Review of personalized recommendation techniques for learners in e-learning systems. Proceeding of International Conference on Semantic Technology and Information Retrieval (STAIR), pp. 277-281.
-          Songjie, G. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software, 5(7), 745-752.
-          Sullivan, M. (2010). Fundamental of Statistics. 3rd Edition, Loose Leaf.
-          Ungar, L., & Foster, D.P. (1998). Clustering Methods for Collaborative Filtering. Processing Workshop on Recommendation Systems at the 15th National Conference on Artificial Intelligence, vol. 1.
-          Ungar, L., & Foster, D.P. (1998). A Formal Statistical Approach to Collaborative Filtering. Proceedings of Conference on Automated Leading and Discovery.
-          Vora, P., & Oza, B. (2013). A Survey on K-mean Clustering and Particle Swarm Optimization. Proceeding of International Journal of Science and Modern Engineering (IJISME), pp. 24-26.
-          Wanqiu, H., Jia, X.,  Tian, F.,  Zhang, Y., &  Zhou, Z. (2015). The Method of Finding Potentially Churning Customers Based on Social Networks. International Journal of Multimedia and Ubiquitous Engineering, 10(11), 95-10, 2015.
-          Wei, S., Ye, N., Zhang, S., Huang, X., & Zhu, J. (2012). Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure. Proceeding of International Conference on Computer Science & Service System (CSSS), pp. 2038-2041.
 
Journal of Industrial and Systems Engineering
Volume 9, Issue 4
October 2016
Pages 9-25

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History

  • Receive Date: 15 November 2015
  • Revise Date: 25 June 2016
  • Accept Date: 16 July 2016

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