Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Collaborative Filtering for Credit Card Recommendation based on Multiple User Profiles

신용카드 추천을 위한 다중 프로파일 기반 협업필터링

  • 이원철 (경일대학교 경영학과) ;
  • 윤협상 (대구가톨릭대학교 경영학부) ;
  • 정석봉 (경일대학교 철도학과)
  • Received : 2017.09.18
  • Accepted : 2017.11.20
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

Keywords

References

  1. Ahn, H.J., A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Information Sciences, 2008, Vol. 1, No. 78, pp. 37-51.
  2. Ali, K. and Stam, W.V., Tivo : Making show recommendations using a distributed collaborative filtering architecture, Proceedings of the Conference on Knowledge Discovery and Data Mining, Washington, USA, 2004, pp. 394-401.
  3. Bell, R., Koren, Y., and Volinsky, C., The Bellkor 2008 Solution to the Netflix Prize, 2008.
  4. Bennet, J. and Lanning, S., The Netflix Prize, Proceedings of the KDD CUP and Workshop, 2007.
  5. Cho, Y.H., Park, S.K., Ahn, D.H., and Kim, J.K., Collaborative Recommendations using Adjusted Product Hierarchy : Methodology and Evaluation, Journal of the Korean Operations Research and Management Science Society, 2004, Vol. 29, No. 2, pp. 61-77.
  6. Choi, J.S., Kim, D.Y., Han, I.S., and Kim, Y.C., A Study on Data Analysis in on/off-line Shopping mall, Electronic Commerce Research Association, 2011, Vol. 12, No. 2, pp. 71-88.
  7. Credit FINANCE Association, http://www.crefia.or.kr.
  8. Das, A., Datar, M., Garg, A., and Rajaram, S., Google news Personalization : Scalable online Collaborative filtering, Proceedings of the World Wide Web Conference, 2003, Budapest, Hungary, pp. 271-280.
  9. Financial Supervisory Service, http://www.fss.or.kr.
  10. Jeong, S.B., The Relationship between Structure Attributes of Product Networks and Performance of integrated Internet Shopping Malls, Association of Business Education, 2015, Vol. 30, No. 3, pp. 97-106.
  11. Kim, C.-J. and Choi, K.-S., A Mobile Web's Recommendation Technique based on XPDL, Journal of the Korea Academia-Industrial Cooperation Society, 2013, Vol. 14, No. 11, pp. 5856-5865. https://doi.org/10.5762/KAIS.2013.14.11.5856
  12. Kim, K.H. and Lee, B.W., A Marketing Study of Credit Card with Aging Society, The Credit Card Review, 2012, Vol. 6, No. 1, pp. 87-105.
  13. Kim, K.-J. and Ahn, H., Collaborative Filtering with a User-Item Matrix Reduction Technique for Recommender Systems, International Journal of Electronic Commerce, 2011, Vol. 16, No. 1, pp. 107-128. https://doi.org/10.2753/JEC1086-4415160104
  14. Kim, K.-J. and Ahn, H., User-Item Matrix Reduction Technique for Personalized Recommender Systems, Journal of Information Technology Applications & Management, 2009, Vol. 16, No. 1, pp. 97-113.
  15. Kim. M. and Cho, Y., A Multimodal Profile Ensemble Approach to Development of Recommender System Using Big Data, The Journal of Intelligence and Information System, 2015, Vol. 21, No. 4, pp. 93-100. https://doi.org/10.13088/JIIS.2015.21.4.093
  16. Lee, J.S. and Park, S.D., Performance Improvement of a Movie Recommendation System using Genre-wise Collaborative Filtering, Journal of Intelligence and Information System, 2007, Vol. 13, No. 4, pp. 65-78.
  17. Lim, C.-H., On-line Recommendation Service Algorithm using Human Sensibility Ergonomics, Journal of the Society of Korea Industrial and Systems Industrial and Systems Engineering, Vol. 27, No. 1, pp 38-46.
  18. Linden, G., Smith, B., and York, J., Amazon.com recommendations : Item-to-item Collaborative filtering, IEEE Internet Computing, 2003, Vol. 7, No. 1, pp. 76-80. https://doi.org/10.1109/MIC.2003.1167344
  19. Nam, K.W., Park, S.S., Shin, Y.K., and Jang, D.S., An Analytical Study on Research trends of Web-based Recommendation System, Proceedings of the Korean Operations Research and Management Science Society Spring Joint Meeting, Pohang, Korea, 2008, pp. 86-97.
  20. Niwattanakul, S., Singthongchai, J., Naenudorn, E., and Wanapu, S., Using of Jaccard Coefficient for Keywords Similarity, Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2013,
  21. Park, J.H., Cho, Y.H., and Kim, J.K., Social Network : A Novel Approach to New Customer Recommendations, Journal of Intelligent Information Systems, 2009, Vol. 15, No. 1, pp. 123-140.
  22. Park, S. and Pennock, D., Applying Collaborative filtering techniques to movie search for better ranking and browsing, Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, California, USA, 2007, pp. 550-559.
  23. Piotte, M. and Chabbert, M., The Pragmatic theory solution to the Netflix grand prize, Netflix prize documentation, 2009.
  24. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., Analysis of Recommendation Algorithm for E-Commerce, Proceedings of the ACME-commerce conference, Minnesota, USA, 2000, pp. 158-167.
  25. Schein, A.I., Popescul, A., Ungarnd, L.H., and Pennock, D.M., Methods and Metrics for Cold-Start Recommendations, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, 2002, pp. 253-260.
  26. Schonberger, V.M. and Cukier, K., Big data : a revolution that will transform how we live work and think, John Murray, 2013.