DOI QR코드

DOI QR Code

Proposal of Content Recommend System on Insurance Company Web Site Using Collaborative Filtering

협업필터링을 활용한 보험사 웹 사이트 내의 콘텐츠 추천 시스템 제안

  • Kang, Jiyoung (Graduate School of Computer & Information Technology, Korea University) ;
  • Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
  • 강지영 (고려대학교 컴퓨터정보통신대학원) ;
  • 임희석 (고려대학교 컴퓨터학과)
  • Received : 2019.09.18
  • Accepted : 2019.11.20
  • Published : 2019.11.28

Abstract

While many users searched for insurance information online, there were not many cases of contents recommendation researches on insurance companies' websites. Therefore, this study proposed a page recommendation system with high possibility of preference to users by utilizing page visit history of insurance companies' websites. Data was collected by using client-side storage that occurs when using a web browser. Collaborative filtering was applied to research as a recommendation technique. As a result of experiment, we showed good performance in item-based collaborative (IBCF) based on Jaccard index using binary data which means visit or not. In the future, it will be possible to implement a content recommendation system that matches the marketing strategy when used in a company by studying recommendation technology that weights items.

Keywords

Recommendation system;Collaborative filtering;IBCF;Jaccard index;Client-side storage

References

  1. J. W. Kim & K. H. Park. (2016). Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern. The Journal of Korean Institute of Communications and Information Sciences, 41(7), 768-774. DOI:10.7840/kics.2016.41.7.68 https://doi.org/10.7840/kics.2016.41.7.768
  2. S. H. Park, J. W. Kim, D. H. Kim & H. J. Cho. (2019). Music Therapy Counseling Recommendation Model Based on Collaborative Filtering. Journal of the Korea Convergence Society, 10(9), 31-36, DOI:10.15207/JKCS.2019.10.9.031
  3. S. K. Gorakala & M. Usuelli. (2015). Building a Recommendation System with R, Birmingham: Packt Publishing.
  4. I. Lim. (2016). Recommendation system using R. Seoul: Chaos Book.
  5. H. J. Sim, M. J. Kim & H. C. Choe. (2018). Consumer's Satisfaction of Insurance Consumption : Focusing on Self-determination Theory. Journal of the Korea Convergence Society, 9(5), 157-169, DOI:10.15207/JKCS.2018.9.5.157
  6. M. Field & V. Stoykov. (2007. June). Online branding: the new frontier[online]. InFinance: The Magazine for Finsia Members, 121(2).
  7. J. H. Park. (2014. Oct). Online Channel Activation Plan by Diversifying Sales Channels. KIRI(Korea Insurance Research Institute) Weekly, 305, 1-4.
  8. E. Y. Bae & S. J. Yu. (2018). Keyword-based Recommender System Dataset Construction and Analysis. Journal of KIIT, 16(6), 91-99. DOI:10.14801/jkiit.2018.16.6.91
  9. S. K. Gorakala & M. Usuelli. (2015). Building a Recommendation System with R, Birmingham: Packt Publishing.
  10. J. W. Choi. (2018). A Study for Improving Sparsity and Scalability Problem in Collaborative Filtering Recommendation System. Master dissertation. Soongsil University, Seoul.
  11. S. R. Jung. (2018). A Study on Improving Efficiency of Recommendation System Using RFM. Journal of the Korean Institute of Plant Engineering, 23(4), 57-64.
  12. Korea Data Agency. (2019). The Guide for Advanced Data Analytics Professional. Seoul: Korea Data Agency
  13. Mozilla contributors. (n.d.). HTTP cookies, MDN Web Docs. https://developer.mozilla.org
  14. H. W. Myeong. J. H. Paik & D. H. Lee. (2012). Study on implementation of Secure HTML5 Local Storage. Journal of Korean Society for Internet Information, 13(4), 83-93. DOI:10.7472/jksii.2012.13.4.83
  15. Y. H. Kim & T. S. Lee. (2014. Aug). Analysis of Major Issues and Vulnerabilities in Internet Cookies. Internet & Security Focus, 79-98.