A Graph Oriented Based Recommender System for Financial Products and Services

A Graph Oriented Based Recommender System for Financial Products and Services

Olakunle Temitope., Awodele, O., Adekunle, Y.A., Eze, M.O., *Ebiesuwa Seun

Department of Computer Science, Babcock University, Nigeria

International Journal of Service Science and Management

Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This research is aimed at developing an intelligent recommender system that provides high quality recommendations in the financial domain. Hashed and anonymized datasets (which are account statements) were acquired from online sources and bank customers. The acquired data was pre-processed using the Microsoft Excel 2016 and WEKA 3.8.3 data mining software. The K-nearest neighbor (KNN) algorithm was used to classify the dataset and train the model. The trained model was used to develop a recommender system using the Java 2 platform Enterprise Edition (J2EE). For effective management of the data and consideration of rapid increase in data growth, a graph-oriented database approach was proposed and utilized. The database management system used was the Neo4j. From the evaluation of the algorithms implemented in the recommender system taxonomy, the KNN algorithm recorded the best performance building the model in 0.3seconds with an accuracy of 89.8%. The fuzzy decision tree algorithm performed second best building the model within 0.48 seconds with an accuracy of 62.8%. The decision table algorithm performed poorly building the model in 3.9 seconds with an accuracy of 53%. However, the baseline accuracy of the dataset used was evaluated to be 62.75% of accuracy in 0.4 seconds. It is therefore recommended, as proposed in this study that the graph technology be used in developing recommender systems especially for institutions with massively growing data like the financial institutions. In addition, bank products should be classified and targeted towards customers in order to bolster their level of involvements and improve financial inclusion. With a targeted product, customers will be more willing to opt-in if products are suitable and within financial reach. This will help financial institutions earn more and the customer’s financial power will also be strengthened.

Keywords:  Graph Oriented Based Recommender System, Financial Products and Services

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How to cite this article:
Olakunle Temitope., Awodele, O., Adekunle, Y.A., Eze, M.O., Ebiesuwa Seun.A Graph Oriented Based Recommender System for Financial Products and Services. International Journal of Service Science and Management, 2020, 3:9


1. Abbas, A., Bilal, K., Zhang, L., & Khan, S. (2015). A cloud-based health insurance plan recommendation system: A user centred approach. Future Generation Computer Systems Journal, 43(44), 99–109
2. Adebayo A, Agbola I, Ayangbade A, & Obajimi O. (2015). Bank Products Recommender. The International Journal of Engineering and Science (IJES), 4(7), 2319–1805. Retrieved from www.theijes.com
3. Aguilar, J., Valdiviezo-Dıaz, P., & Riofrio, G. (2016). A general framework for intelligent recommender systems. Applied Computing and Informatics. 13, 147–160
4. Altman, N. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 46 (3), 175–185. doi:10.1080/00031305.1992.10475879.
5. Anyanwu, M., & Shiva, A., (2009). Comparative Analysis of Serial Decision Tree Classification Algorithms. International Journal of Computer Science and Security. 3, 230-237
6. Apte, C., Hong, S., Natarajan, R., Pednault, E., Tipu, F., & Weiss, S. (2002). Data Intensive Analytics for Predictive Modelling. Retrieved December, 11, 2018, from http://www.research.ibm.com/dar /papers/pdf/dar_overview_ibmjrd .pdf
7. Brownlee, J., (2016). K-Nearest Neighbours for Machine Learning. Understand Machine Learning Algorithms.
8. Case of Retail and Banking Service in Iran (2008). World Scientific and Engineering Academy and Society (WSEAS). Transactions on Business and Economics, 5(5), 189–200.
9. Chih-Min M., Wei-Shui Y. & Bor-Wen C. (2014). How the parameters of K-nearest neighbor algorithm impact on the best classification accuracy: In case of parkinson dataset. Journal of Applied Sciences, 14: 171-176.
10. Smyth, B. (2007). Case-based recommendation, in the adaptive web, 342–376, Springer
11. Swearingen, K., & Sinha, R., (2002). Interaction design for recommender systems. in In Designing Interactive Systems. ACM. Press.
12. Taiwo, O. (2009). Machine learning overview University of Portsmouth United Kingdom
13. Tao, W., Zhoujun, L., Yuejin, Y., & Huowang, C. (2007), A Survey of Fuzzy Decision Tree Classifier Methodology.B.-Y. Cao (Ed.), Fuzzy Information and Engineering (ICFIE), ASC 40, 959–968.
14. Tintarev, N., & Masthoff, J., (2011). Designing and evaluating explanations for recommender systems. in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer US, 479–510.
15. Trewin, S., (2000). Knowledge-based recommender systems. Encyclopedia of library and information science, 69(Supplement 32), 180
16. Vico, D., Huecas, G., & Rodr´ıguez, J. (2012). Generating Context-aware recommendations using Banking data in a mobile recommender system. The Sixth International Conference on Digital Society. ISBN: 978-1-61208-176-2. 73-78
17. Wang, L., Hu, X., Wei, J., & Cui, X. (2013). A Collaborative Filtering Based Personalized TOP-K Recommender System for Housing. in Proceedings of the 2012 International Conference of Modern Computer Science and Applications SE – 74, ed., Zhenyu Du, volume 191 of Advances in Intelligent Systems and Computing, 461–466, Springer Berlin Heidelberg.
18. Yahyapour, N. (2008). Determining Factors Affecting Intention to Adopt Banking Recommender System’, Technology.
19. Yu, C. (2004). Designing a consumer-oriented intelligent decision support system for personalized financial planning services.
20. Yuan, X., Lee, J., Kim, S., & Kim, Y. (2013). Toward a user-oriented recommendation system for real estate websites. Information Systems, 38(2), 231–243
21. Zhao, X., Weinan, Z., & Jun, W. (2015). Risk-Hedged venture capital investment recommendation. Proceedings of the 9th ACM Conference on Recommender Systems, RecSys ’15, 75–82, New York, USA,