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Bankacılık Sektöründe Tavsiye Sistemleri: Bir Literatür Çalışması

Yıl 2023, Cilt: 6 Sayı: 2, 56 - 66, 21.12.2023

Öz

Tavsiye sistemleri, günümüzde hızla artan kullanıcı ve ürün veri havuzlarını işlemek üzere sofistike algoritmaların kullanıldığı ve ürünler ile müşteriler arasında en uyumlu eşleşmeyi bulmayı amaçlayan bir teknolojidir. Farklı sektörlerde çeşitli ürün tipleri için uygulanabilse de, ana hedef her zaman müşteri memnuniyetini optimize etmek, kişiselleştirilmiş bir kullanıcı deneyimi sunmak ve satışları artırmaktır. Bu çalışma, bankacılık sektöründeki tavsiye sistemlerine odaklanır ve mevcut literatürü derinlemesine inceleyerek kullanılan veri setlerini ve yöntemleri ele alır. Çalışmanın amacı, bankacılık sektöründe tavsiye sistemlerinin potansiyel faydalarını ortaya koymak ve gelecekteki araştırmalar için potansiyel alanları belirlemektir.

Destekleyen Kurum

Vakıfbank

Kaynakça

  • [1] Ricci, F., Rokach, L., Shapira, B. and Kantor, P. B, Recommender Systems Handbook. Boston, MA: Springer US. doi: 10.1007/978-0-387-85820-3. 2011.
  • [2] Dong, Z Wang, X Xu, Jun Tang, R Wen, J, A Brief History of Recommender Systems, s. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages, 2022.
  • [3] Goldberg, D. Nichols, D. Oki, B. M. and Terry, D., Using collaborative filtering to weave an information tapestry. Communications of The ACM 35, 12 (1992), 61–70, 1992.
  • [4] Resnick, P. Iacovou, N. Suchak, M. Bergstrom, P. and Riedl, J., GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Research Papers in Economics, 1994.
  • [5] Recommendations: What and Why?, Recommendation Systems Google Developers. https://developers.google.com/machine-learning/recommendation/overview (18.11.2022).
  • [6] Schafer, J.B., Konstan, J.A. & Riedl, J. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5, 115–153. https://doi.org/10.1023/A:1009804230409, 2001.
  • [7] Felfernig, A. , Koba4ms: Selling complex products and services using knowledge-based recommender technologies. In: CEC ’05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology (CEC’05), pp. 92–100. IEEE Computer Society, 2005.
  • [8] Felfernig A. and Kiener A., Knowledge-based Interactive Selling of Financial Services with FSAdvisor, American Association for Artificial Intelligence (www.aaai.org), 100, 1475–1482, 2005.
  • [9] Sharaf, M. Hemdan, E.E.D. El-Sayed, A ve El-Bahnasawy, N. A.. A survey on recommendation systems for financial services Multimedia Tools and Applications (2022) 81:16761–16781, 2022.
  • [10] Zibriczky, D. Recommender Systems meet Finance: A literature review, in. doi: 10.13140/RG.2.1.1249.2405, 2016.
  • [11] Sharifihosseini, A. . A Case Study for Presenting Bank Recommender Systems based on Bon Card Transaction Data. 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019.
  • [12] Y. Koren, R. Bell and C. Volinsky, "Matrix Factorization Techniques for Recommender Systems," in Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009, doi: 10.1109/MC.2009.263, 2009.
  • [13] Lorenzi, F. and Ricci, F., Case-Based Recommender Systems: a Unifying View, in Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization, ITWP’03, pp. 89–113, Berlin, Heidelberg, 2003.
  • [14] Musto, C. Semeraro G. Lops, P. Gemmis, M. ve Lekkas G., ‘Personalized finance advisory through case-based recommender systems and diversification strategies’, Decision Support Systems, 77, 100–111, 2015.
  • [15] Santander Product Recommendation, Can you pair products with people? https://www.kaggle.com/c/santander-product-recommendation (18.11.2022).
  • [16] Oyebode, O., & Orji, R., A hybrid recommender system for product sales in a banking environment. Journal of Banking and Financial Technology, 4(1), 15–25. https://doi.org/10.1007/s42786-019-00014-w, 2020.
  • [17] Vaquero-Patricio, C. Ommeren N. Gil-Begue S. Recommenders in Banking: An End-to-end Personalization Pipeline within ING, RecSys ’21, September 27-October 1, 2021, Amsterdam, Netherlands, 2021.
  • [18] Fano, A. and Kurth, Scott W, Personal Choice Point: Helping users visualize what it means to buy a BMW’, Control, 46–52, 2003.
  • [19] Gallego, V. D. ve Huecas, G., Generating Context-aware Recommendations using Banking Data in a Mobile Recommender System, ICDS 2012 : The Sixth International Conference on Digital Society Generating, 73–78, 2012.
  • [20] Gigli, A., Lillo, F., & Regoli, D., Recommender systems for banking and financial services. Conference on Recommender Systems, 1905, 1–2. http://ceur-ws.org/Vol-1905/recsys2017_poster13.pdf, 2017.
  • [21] Jaramillo, I.F., Villarroel-Molina, R., Pico, B.R., Redchuk, A.. A Comparative Study of Classifier Algorithms for Recommendation of Banking Products. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_25, 2021.
  • [22] Santander Product Recommendation | Kaggle.. https://www.kaggle.com/competitions/santander-product-recommendation/discussion/26835 (26.08.2023).
  • [23] Ghobakhloo, M., Ghobakhloo, M., Design of a personalized recommender system using sentiment analysis in social media (case study: banking system), Social Network Analysis and Mining (2022) 12:84, 2022.
  • [24] Hernández-Nieves E., Hernández, G., Gil-González, A.B., Rodríguez-González S. Corchado, J. M., Fog computing architecture for personalized recommendation of banking products, 2019.
  • [25] Liu, D. Farajalla, G. P. ve Boulenger, A., BRec the Bank: Context-aware Self-attentive Encoder for Banking Products Recommendation, 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1-8, 2022.
  • [26] H. A. C. Okan Sakar, "A Dynamic Recurrent Neural Networks-Based Recommendation System for Banking Customers," 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477967, 2021.

Recommendatıon Systems In Bankıng – A Lıteratur Revıew

Yıl 2023, Cilt: 6 Sayı: 2, 56 - 66, 21.12.2023

Öz

Recommendation systems are a technology aimed at finding the most compatible match between products and customers, using sophisticated algorithms to process the rapidly growing pools of user and product data in today's world. While they can be applied to various product types across different sectors, the primary goal is always to optimize customer satisfaction, provide a personalized user experience, and boost sales. This study focuses on recommendation systems in the banking sector, delving deeply into the current literature while examining the datasets and methods used. The aim of the research is to highlight the potential benefits of recommendation systems in the banking industry and identify potential areas for future research.

Kaynakça

  • [1] Ricci, F., Rokach, L., Shapira, B. and Kantor, P. B, Recommender Systems Handbook. Boston, MA: Springer US. doi: 10.1007/978-0-387-85820-3. 2011.
  • [2] Dong, Z Wang, X Xu, Jun Tang, R Wen, J, A Brief History of Recommender Systems, s. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages, 2022.
  • [3] Goldberg, D. Nichols, D. Oki, B. M. and Terry, D., Using collaborative filtering to weave an information tapestry. Communications of The ACM 35, 12 (1992), 61–70, 1992.
  • [4] Resnick, P. Iacovou, N. Suchak, M. Bergstrom, P. and Riedl, J., GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Research Papers in Economics, 1994.
  • [5] Recommendations: What and Why?, Recommendation Systems Google Developers. https://developers.google.com/machine-learning/recommendation/overview (18.11.2022).
  • [6] Schafer, J.B., Konstan, J.A. & Riedl, J. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5, 115–153. https://doi.org/10.1023/A:1009804230409, 2001.
  • [7] Felfernig, A. , Koba4ms: Selling complex products and services using knowledge-based recommender technologies. In: CEC ’05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology (CEC’05), pp. 92–100. IEEE Computer Society, 2005.
  • [8] Felfernig A. and Kiener A., Knowledge-based Interactive Selling of Financial Services with FSAdvisor, American Association for Artificial Intelligence (www.aaai.org), 100, 1475–1482, 2005.
  • [9] Sharaf, M. Hemdan, E.E.D. El-Sayed, A ve El-Bahnasawy, N. A.. A survey on recommendation systems for financial services Multimedia Tools and Applications (2022) 81:16761–16781, 2022.
  • [10] Zibriczky, D. Recommender Systems meet Finance: A literature review, in. doi: 10.13140/RG.2.1.1249.2405, 2016.
  • [11] Sharifihosseini, A. . A Case Study for Presenting Bank Recommender Systems based on Bon Card Transaction Data. 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019.
  • [12] Y. Koren, R. Bell and C. Volinsky, "Matrix Factorization Techniques for Recommender Systems," in Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009, doi: 10.1109/MC.2009.263, 2009.
  • [13] Lorenzi, F. and Ricci, F., Case-Based Recommender Systems: a Unifying View, in Proceedings of the 2003 International Conference on Intelligent Techniques for Web Personalization, ITWP’03, pp. 89–113, Berlin, Heidelberg, 2003.
  • [14] Musto, C. Semeraro G. Lops, P. Gemmis, M. ve Lekkas G., ‘Personalized finance advisory through case-based recommender systems and diversification strategies’, Decision Support Systems, 77, 100–111, 2015.
  • [15] Santander Product Recommendation, Can you pair products with people? https://www.kaggle.com/c/santander-product-recommendation (18.11.2022).
  • [16] Oyebode, O., & Orji, R., A hybrid recommender system for product sales in a banking environment. Journal of Banking and Financial Technology, 4(1), 15–25. https://doi.org/10.1007/s42786-019-00014-w, 2020.
  • [17] Vaquero-Patricio, C. Ommeren N. Gil-Begue S. Recommenders in Banking: An End-to-end Personalization Pipeline within ING, RecSys ’21, September 27-October 1, 2021, Amsterdam, Netherlands, 2021.
  • [18] Fano, A. and Kurth, Scott W, Personal Choice Point: Helping users visualize what it means to buy a BMW’, Control, 46–52, 2003.
  • [19] Gallego, V. D. ve Huecas, G., Generating Context-aware Recommendations using Banking Data in a Mobile Recommender System, ICDS 2012 : The Sixth International Conference on Digital Society Generating, 73–78, 2012.
  • [20] Gigli, A., Lillo, F., & Regoli, D., Recommender systems for banking and financial services. Conference on Recommender Systems, 1905, 1–2. http://ceur-ws.org/Vol-1905/recsys2017_poster13.pdf, 2017.
  • [21] Jaramillo, I.F., Villarroel-Molina, R., Pico, B.R., Redchuk, A.. A Comparative Study of Classifier Algorithms for Recommendation of Banking Products. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_25, 2021.
  • [22] Santander Product Recommendation | Kaggle.. https://www.kaggle.com/competitions/santander-product-recommendation/discussion/26835 (26.08.2023).
  • [23] Ghobakhloo, M., Ghobakhloo, M., Design of a personalized recommender system using sentiment analysis in social media (case study: banking system), Social Network Analysis and Mining (2022) 12:84, 2022.
  • [24] Hernández-Nieves E., Hernández, G., Gil-González, A.B., Rodríguez-González S. Corchado, J. M., Fog computing architecture for personalized recommendation of banking products, 2019.
  • [25] Liu, D. Farajalla, G. P. ve Boulenger, A., BRec the Bank: Context-aware Self-attentive Encoder for Banking Products Recommendation, 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1-8, 2022.
  • [26] H. A. C. Okan Sakar, "A Dynamic Recurrent Neural Networks-Based Recommendation System for Banking Customers," 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477967, 2021.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tavsiye Sistemleri
Bölüm Makaleler
Yazarlar

Yudum Paçin

Yayımlanma Tarihi 21 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 2

Kaynak Göster

APA Paçin, Y. (2023). Bankacılık Sektöründe Tavsiye Sistemleri: Bir Literatür Çalışması. Veri Bilimi, 6(2), 56-66.



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