Motaharinejad, Maryam sadat, Zolfagharzadeh, Mohammad Mahdi, Khadangi, Ehsan, Sadabadi, Ali Asghar. (1395). Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques. , 8(2), 393-314. doi: 10.22059/jitm.2016.57230
Maryam sadat Motaharinejad; Mohammad Mahdi Zolfagharzadeh; Ehsan Khadangi; Ali Asghar Sadabadi. "Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques". , 8, 2, 1395, 393-314. doi: 10.22059/jitm.2016.57230
Motaharinejad, Maryam sadat, Zolfagharzadeh, Mohammad Mahdi, Khadangi, Ehsan, Sadabadi, Ali Asghar. (1395). 'Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques', , 8(2), pp. 393-314. doi: 10.22059/jitm.2016.57230
Motaharinejad, Maryam sadat, Zolfagharzadeh, Mohammad Mahdi, Khadangi, Ehsan, Sadabadi, Ali Asghar. Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques. , 1395; 8(2): 393-314. doi: 10.22059/jitm.2016.57230

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

Journal of Information Technology Management
مقاله 70، دوره 8، شماره 2، 2016، صفحه 393-314    اصل مقاله (859.39 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2016.57230
نویسندگان
Maryam sadat Motaharinejad1؛ Mohammad Mahdi Zolfagharzadeh* 2؛ Ehsan Khadangi3؛ Ali Asghar Sadabadi4
1MSc in Information Technology Management, Islamic Azad University E-campus, Tehran, Iran
2Assistant Prof., Faculty of New Sciences and Technologies, University of Tehran, Iran
3Ph.D. Student in Computer Engineering, Amirkabir University of Technology, Tehran, Iran
4PhD student in science and technology policy, Faculty of New Sciences and Technologies, University of Tehran, Tehran
چکیده
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.
 
کلیدواژه‌ها
Classification؛ Data Mining؛ E-Banking؛ Recommender System
مراجع
آمار
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