A Model of Intelligent Recommender System With Explicit Feedback Mechanism for Performance Improvement

A Model of Intelligent Recommender System With Explicit Feedback Mechanism for Performance Improvement

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

Department of Computer Science, Babcock University, Nigeria

Global Journal of Artificial Intelligence

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 work focused on using users bank statements that explicitly shows inflow and outflow of funds. The dataset used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as a web application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used. In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart amongst others impelled the research community to avail similar benefits in financial domain to recommend product and services (Lim, 2015). Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favourably evaluated and accepted by its users. This work provides detailed descriptions of methods employed to proffer solutions to intelligent recommender system with explicit feedback mechanism. The methodology of this research work refers to the research approach adopted by the researcher to tackle the research problem as stated in earlier chapter. Since the efficiency and maintainability of any application is solely dependent on how the designs are prepared, this chapter describes the various processes, methods and procedures used to achieve set objectives and the conceptual structure within which the research was conducted.

Keywords:  Intelligent Recommender System, Explicit Feedback Mechanism, Performance Improvement

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How to cite this article:
Olakunle Temitope., Awodele, O., Adekunle, Y.A., Eze, M.O., Ebiesuwa Seun. A Model of Intelligent Recommender System With Explicit Feedback Mechanism for Performance Improvement. Global Journal of Artificial Intelligence, 2020; 2:6.


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. Asosheh, A, Bagherpour, S., Yahyapou, A., & Asosheha. A. (2008). Extended Acceptance Models for Recommender System Adaption.
8. Bhaskar, T., & Subramanian, G. (2011). Loan recommender system for microfinance loans: Increasing efficiency to assist growth. Journal of Financial Services Marketing, 15(4), 334–345.
9. Brownlee, J., (2016). K-Nearest Neighbours for Machine Learning. Understand Machine Learning Algorithms.
10. 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.
11. 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.
12. Fano, A., & Kurth, S. (2003). Personal Choice Point: Helping users visualize what it means to buy a BMW. Control, 46–52.
13. Farajian, M. A., & Mohammadi, S. (2019). Mining the banking customer behavior using clustering and association methods. International Journal of Industrial Engineering & Production Research, 21(4).
14. Felfernig, A., & Kiener, A. (2005). Knowledge-based interactive selling of financial services with FSAdvisor. Proceedings of the National …, 100, 1475–1482.
15. Felfernig, A., & Stettinger, M. (2015). Conflict Management in Interactive Financial Service Selection. Organizational Support, 3.
16. Felfernig, A., Isak, K., Szabo, K., & Zachar, U. (2007). The VITA financial services sales support environment. Proceedings of the National Conference on Artificial Intelligence, 2(1), 1692–1699.
17. Felfernig, A., Jeran, M., Stettinger, M., Absenger, T., Gruber, T., & Haas, S. (2015). Human computation based acquisition of financial service advisory practices. Proceedings of the 1st International Workshop on Personalization & Recommender Systems in Financial Services
18. Felfernig, A., & Burke, R. (2008). Constraint-based recommender systems: Technologies and research issues. 10th International Conference on Electronic Commerce, 3. ACM.
19. Gallego, D., & Huecas, G. (2012). An empirical case of a context aware mobile recommender system in a banking environment. Proceedings. 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2012, 13–20.
20. Gigli, A., Lillo, F., & Regoli, D. (2017). Recommender Systems for Banking and Financial Services. In Proceedings of RecSys Posters, Como, Italy.
21. Ginevicius, T., Alchimovien, J., Kazokaitis, P., & Kaklauskas, A. (2011). Recommender System for Real Estate Management. Verslas: teorija ir praktika, (3), 258–267.
22. Gulzar, Z., Leema, A., & Deepak, G. (2018). PCRS: Personalized Course Recommender System Based on Hybrid Approach. Procedia Computer Science, 6th International Conference on Smart Computing and Communications, ICSCC 2017, 125(20) 518–524. 7-8 December 2017, Kurukshetra, India.
23. Guo, G., Zhang, J., & Yorke-Smith, N. (2015). Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-based Systems 74, 14–27.
24. Gupta, A., & Jain, A., (2013). Life Insurance Recommender System Based on Association Rule Mining and Dual Clustering Method for Solving Cold-Start Problem. International Journal of Advanced Research in Computer Science and Software Engineering, 3(7), 1356 – 1360
25. Jallouli, M., Lajmi, S., & Amous, I. (2017). Designing Recommender System: Conceptual Framework and Practical Implementation. International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2017, 6-8 September 2017, Marseille, France Procedia Computer Science 112 (17), 1701–1710
26. Janikow, C. (1998). Fuzzy Decision Trees: Issues and Methods. IEEE Transactions on Systems, Man and Cybernetics, 28(1): 1–14., New Jersey, USA.
27. Musto, C., Lops, P., Gemmis, M., Lekkas, G., & Semeraro, G. (2015). Personalized finance advisory through case-based recommender systems and diversification strategies. Decision Support Systems, 77, 100-111
28. Palomares, I., & Kovalchuk, S., (2017). Multi-View Data approaches in Recommender Systems: an overview. International Young Scientists Conference in HPC and Simulation, YSC 2017, Kotka, Finland.
29. Pazzani, M., & Billsus, D. (2007). Content-based recommendation systems, in the adaptive web, 325–341, Springer
30. Peng, W., Chen, J., & Zhou, H. (2006). An Implementation of ID3 – Decision Tree Learning Algorithm. University of New South Wales. Retrieved October 11, 2018, from http://web.arch.usyd.edu.au/~wpeng/DecisionTree2.pdf
31. Press-William, H., Saul, A., William, T., Flannery, O., & Brian, P. (2007). Support Vector Machines. Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press.

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