Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques

Document Type : Research Paper

Authors

1 MSc in Information Technology Management, Islamic Azad University E-campus, Tehran, Iran

2 Assistant Prof., Faculty of New Sciences and Technologies, University of Tehran, Iran

3 Ph.D. Student in Computer Engineering, Amirkabir University of Technology, Tehran, Iran

4 PhD student in science and technology policy, Faculty of New Sciences and Technologies, University of Tehran, Tehran

Abstract

Nowadays, banks require new devices such as recommender systems to attract and preserve customers. Unlike most recommender systems in which the given recommendation is based on similarities between the preferences of users, this research has employed the classification techniques where customer’s past interests is considered as the most important feature to provide proper banking services for them. In this research, four classifiers including MLP, SVM, KNN, and Naïve Bayes have been used.  Firstly, the data set which was related to the services used by different bank customers was pre-processed and four different classification methods were trained by using it. Then, their validations were assessed by the 10-fold cross validation and the best method was selected. Lastly, the final recommender system which was a combination of four classification methods including Naïve Bayes with performance P=%85.4, 5-nn with P=%83.3, MLP with P=%81.4, and MLP with P=%92.6 respectively proposed for recommendation of four banking services including the internet, mobile, internet transfer and paying on the phone is.
 

Keywords

Main Subjects


Asosheha, A., Bagherpour, S. & Yahyapour, N. (2008). Extended acceptance models for recommender system adaption, case of retail and banking service in Iran. WSEAS transactions on business and economics, 5(5): 189-200.
Breunig, M. M., Kriegel, H. P., Ng, R. T. & Sander, J. (2000). LOF: identifying density-based local outliers. in ACM Sigmod Record, 29(2): 93-104.
Chen, W. & Fong, S. (2010). Social network collaborative filtering framework and online trust factors: a case study on Facebook. The Fifth International Conference on Digital Information Management (ICDIM). Thunder Bay, 5-8 July 2010.
Gallego, D., Huecas, G. & Salvachúa Rodríguez, J. (2012). Generating context-aware recommendations using banking data in a mobile recommender system. The Sixth International Conference on Digital Society, Valencia, 30 January - 4 February 2012.
Gallego, D. & Huecas, G. (2012). An empirical case of a context-aware mobile recommender system in a banking environment. Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing. Vancouver, 26-28 June 2012.
Han, J. & Kamber, M. & Pei, J. (2012). Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition. Amsterdam: Elsevier.
Horozov, T., Narasimhan, N. & Vasudevan, V. (2006). Using location for personalized POI recommendations in mobile environments. International Symposium on Applications and the Internet, Phoenix, 23-27 January 2006.
Hsu, W. H., King, A. L, Paradesi, M. S., Pydimarri, T. & Weninger, T. (2006). Collaborative and Structural Recommendation of Friends using Weblog-based Social Network Analysis. in AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. Palo Alto, 27–29 March 2006.
Huang, Z., Zeng, D. D. & Chen, H. (2007) . Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science, 53(7): 1146-1164.
Kangas, S. (2002). Collaborative filtering and recommendation systems. in: VTT information technology. Espoo: VTT.
Karimi Alavije, M., Askari, S. & Parasite, S. (2015). Intelligent Online Store: User Behavior Analysis based Recommender System. Journal of Information Technology Management. 7(2): 385-406.
Kim, Y. S., Yum, B. J., Song, J. & Kim, S. M. (2005). Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Systems with Applications. 28(1): 381-393.
Kompan, M. & Bieliková, M. (2010). Content-based news recommendation. International Conference on Electronic commerce and web technologies (EC-Web 2010), University of Deusto, Bilbao, 30 August - 3 September 2010.
Konow, R., Tan, W., Loyola, L., Pereira, J. & Baloian, N. (2011). A Visited Item Frequency Based Recommender System: Experimental Evaluation and Scenario Description. Journal of Universal Computer Science, 17(14): 2009-2028.
Martín-Guerrero, J. D. & Lisboa, P. J. & Soria-Olivas, E. & Palomares, A. & Balaguer, E. (2007). An approach based on the Adaptive Resonance Theory for analyzing the viability of recommender systems in a citizen Web portal. Expert Systems with Applications, 33(3): 743-753.
Radfar, R. & Nezafati, N. & Yousefi Asli, S. (2014). Classification of Internet banking customers using data mining algorithms. Journal of Information Technology Management, 6(1): 71-90. (in Persian)
Rasoli, H. & Manian, A. (2012). Designing a Fuzzy Inference System for Selecting e-Banking Services (Case Study: Sepah Bank). Journal of Information Technology Management, 4(12): 41-64.
Shabib, N. & Nematbakhsh, M. & Ghahramani, F. (2009). Using data mining methods for recognizing customer behavior in mobile commerce. The Second Iran data mining conference. (in Persian)
Shakouri, H., Amiri, B., Mousakhani, M. & Khadangi, E. (2016). Presenting a model for predicting needed technologies in banks using SOM and ARM. Knowledge & Technology, in Press. (in Persian)
Taghva, M. R., Bamakan, S. M. H. & Toufani, S. (2011). A data mining method for service marketing: A case study of banking industry. Management Science Letters, 1(3): 253-262.
Wang, Z., Sun, L., Zhu, W., Yang, S., Li, H. & Wu, D. (2013). Joint social and content recommendation for user-generated videos in online social network. IEEE Transactions on Multimedia, 15(3): 698-709.
Yin, D. & Hong, L. & Davison, B. D. (2011). Structural link analysis and prediction in microblogs. Proceedings of the 20th ACM international conference on Information and knowledge management. Glasgow, 24-28 October 2011.
Zeinolabedin, F. & Mahdavi, M. & Khanbabaee, M. (2012). Customers clustering using data mining techniques and RFM model to marketing banking services. The 4th international conference on Banking Services marketing. Tehran, 14-15 October 2012. (in Persian)