Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques
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 pa...
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| Format: | Article |
| Language: | English |
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University of Tehran
2016-07-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_57230_10b61b9461be0adc2ebce9552302719a.pdf |
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| author | Maryam sadat Motaharinejad Mohammad Mahdi Zolfagharzadeh Ehsan Khadangi Ali Asghar Sadabadi |
| author_facet | Maryam sadat Motaharinejad Mohammad Mahdi Zolfagharzadeh Ehsan Khadangi Ali Asghar Sadabadi |
| author_sort | Maryam sadat Motaharinejad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ae65b7f423ba449c8e415e755de10c31 |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-ae65b7f423ba449c8e415e755de10c312025-08-20T02:23:35ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592016-07-018239331410.22059/jitm.2016.5723057230Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining TechniquesMaryam sadat Motaharinejad0Mohammad Mahdi Zolfagharzadeh1Ehsan Khadangi2Ali Asghar Sadabadi3MSc in Information Technology Management, Islamic Azad University E-campus, Tehran, IranAssistant Prof., Faculty of New Sciences and Technologies, University of Tehran, IranPh.D. Student in Computer Engineering, Amirkabir University of Technology, Tehran, IranPhD student in science and technology policy, Faculty of New Sciences and Technologies, University of Tehran, TehranNowadays, 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.https://jitm.ut.ac.ir/article_57230_10b61b9461be0adc2ebce9552302719a.pdfClassificationData MiningE-BankingRecommender System |
| spellingShingle | 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 Journal of Information Technology Management Classification Data Mining E-Banking Recommender System |
| title | Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques |
| title_full | Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques |
| title_fullStr | Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques |
| title_full_unstemmed | Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques |
| title_short | Designing a Model for Improving Banking Recommender Systems Based on Predicting Customers’ Interests: Application of Data Mining Techniques |
| title_sort | designing a model for improving banking recommender systems based on predicting customers interests application of data mining techniques |
| topic | Classification Data Mining E-Banking Recommender System |
| url | https://jitm.ut.ac.ir/article_57230_10b61b9461be0adc2ebce9552302719a.pdf |
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