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A hybrid recommender system for product sales in a banking environment

  • Oladapo OyebodeEmail author
  • Rita Orji
Original Paper
  • 10 Downloads

Abstract

Recommender systems have been successfully applied in many domains, including in e-commerce and entertainment to boost sales. However, most existing recommender systems employ the collaborative or community-based approach which relies on the preferences or behaviour of other users, in conjunction with that of the target user. This approach may not be applicable to domains such as banking. The two major challenges to using the collaborative approach in the banking domain are: (1) absence of explicit ratings of products by customers for recommendation purpose, (2) cold-start problem which makes it difficult to recommend products to a prospective or new customer who has no preference at all. To tackle the first issue, we develop an algorithm that implicitly infers customer preferences from transaction data. To address the second issue, we propose a hybrid recommender system that combines the item-based collaborative filtering technique (which uses the customer preference data from the algorithm) and the demographic-based approach (which uses customers’ demographics). We contribute to knowledge by developing a practical and feasible approach for implementing recommender systems that drive product marketing in the banking sector. We also discuss the performance of this approach based on 393,816 customers dataset. The hybrid approach is applicable to other domains with similar challenges.

Keywords

Recommender system Hybrid approach Item-based collaborative filtering Demographic-based technique Product Banking Unsupervised machine learning 

References

  1. 1.
    Aerts L, Claes A, Leeuw D de, Leeuw E de (2019) Implicit hybrid recommender system for informative articles. In: Seminar case studies in business analytics and quantitative marketing (FEM21001), pp 1–52. https://www.researchgate.net/publication/331716098_Implicit_Hybrid_Recommender_System_for_Informative_Articles
  2. 2.
    Aggarwal CC (2016) Recommender systems. Springer, Switzerland.  https://doi.org/10.1007/978-3-319-29659-3 CrossRefGoogle Scholar
  3. 3.
    Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR '99 workshop on recommender systems: algorithms and evaluation. https://www.semanticscholar.org/paper/Combining-Content-Based-and-Collaborative-Filters-Claypool-Gokhale/21cefe0b5fbd4a7bd258d25255f6bfce4dca3306
  4. 4.
    EMC Corporation (2013) Banking on customer behaviour. https://docplayer.net/13040426-Banking-on-customer-behavior.html
  5. 5.
    Gallego D, Huecas G (2012) An empirical case of a context-aware mobile recommender system in a banking environment. In: 2012 third FTRA international conference on mobile, ubiquitous, and intelligent computing. IEEE, Vancouver, pp 13–20.  https://doi.org/10.1109/MUSIC.2012.11 CrossRefGoogle Scholar
  6. 6.
    Gupta J, Gadge J (2014) A framework for a recommendation system based on collaborative filtering and demographics. In: 2014 international conference on circuits, systems, communication and information technology applications (CSCITA). IEEE, Mumbai, pp 300–304.  https://doi.org/10.1109/CSCITA.2014.6839276 CrossRefGoogle Scholar
  7. 7.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22:5–53.  https://doi.org/10.1145/963770.963772 CrossRefGoogle Scholar
  8. 8.
    Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, New YorkGoogle Scholar
  9. 9.
    Jolliffe IT (2002) Principal component analysis. Springer, New York.  https://doi.org/10.1007/b98835 CrossRefzbMATHGoogle Scholar
  10. 10.
    Kodinariya TM, Makwana PR (2013) Review on determining number of Cluster in K-Means clustering. Int J Adv Res Comput Sci Manag Stud 1:2321–7782Google Scholar
  11. 11.
    Larkey LS, Croft WB (1996) Combining classifers in text categorization. 19th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, New York, pp 289–297Google Scholar
  12. 12.
    Mitra S, Chaudhari N, Patwardhan B (2014) Leveraging hybrid recommendation system in insurance domain. Int J Eng Comput Sci 3:8988–8992Google Scholar
  13. 13.
    Passi R, Jain S, Singh PK (2019) Hybrid approach for recommendation system. In: 2nd international conference on data engineering and communication technology. Springer, Singapore, pp 117–128.  https://doi.org/10.1007/978-981-13-1610-4_12 CrossRefGoogle Scholar
  14. 14.
    Pazzani MJ (1999) Framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13:393–408.  https://doi.org/10.1023/A:1006544522159 CrossRefGoogle Scholar
  15. 15.
    Rahman MM, Zaki ZBM, Alwi NHBM, Monirul Islam M (2019) A hybrid approach to improve recommendation system in E-tourism. In: Emerging technologies in data mining and information security. Springer, Singapore, pp 787–797.  https://doi.org/10.1007/978-981-13-1951-8_70 CrossRefGoogle Scholar
  16. 16.
    Ricci F, Rokach L, Shapira B (2015) Recommender systems handbook. Springer, New York.  https://doi.org/10.1007/978-1-4899-7637-6 CrossRefzbMATHGoogle Scholar
  17. 17.
    Salam Z, Najafi S (2016) Evaluating prediction accuracy for collaborative filtering algorithms in recommender systems. KTH Royal Institute of Technology, Stockholm. http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A927356&dswid=537 Google Scholar
  18. 18.
    Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: 10th international conference on World Wide Web (WWW ’01). ACM, New York, pp 285–295.  https://doi.org/10.1145/371920.372071 CrossRefGoogle Scholar
  19. 19.
    Sayyed FR, Argiddi RV, Apte SS (2013) Generating recommendations for stock market using collaborative filtering. Int J Comput Eng Sci 3:46–49Google Scholar
  20. 20.
    Smith J, Weeks D, Jacob M, Freeman J, Magerko B (2019) Towards a hybrid recommendation system for a sound library. In: ACM IUI workshops. Los Angeles, USA. http://ceur-ws.org/Vol-2327/IUI19WS-MILC-5.pdf
  21. 21.
    Vozalis M, Margaritis KG (2004) Collaborative filtering enhanced by demographic correlation. In: AIAI symposium on professional practice in AI, part of the 18th World Computer Congress. https://pdfs.semanticscholar.org/989a/4873e11df7e20cd856c618fce1828e85b489.pdf
  22. 22.
    Xie F, Xu M, Chen Z (2012) RBRA: A simple and efficient rating-based recommender algorithm to cope with sparsity in recommender systems. In: Proceedings of 26th IEEE international conference on advanced information networking and applications workshops, WAINA, pp 306–311.  https://doi.org/10.1109/WAINA.2012.11
  23. 23.
    Ye BK, Tu YJ, Liang TP (2019) A hybrid system for personalized content recommendation. J Electron Commer Res 20:91–104Google Scholar
  24. 24.
    Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X (2014) We know what you want to buy: a demographic-based system for product recommendation on microblogs. Proc ACM SIGKDD Int Conf Knowl Discov Data Min.  https://doi.org/10.1145/2623330.2623351 CrossRefGoogle Scholar

Copyright information

© Institute for Development and Research in Banking Technology 2020

Authors and Affiliations

  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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