Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

Addressing the Cold Start Problem of Recommendation Method based on App

초기 사용자 문제 개선을 위한 앱 기반의 추천 기법

  • 김성림 (서일대학교 소프트웨어공학과) ;
  • 권준희 (경기대학교 컴퓨터공학부)
  • Received : 2019.07.23
  • Accepted : 2019.09.03
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The amount of data is increasing significantly as information and communication technology advances, mobile, cloud computing, the Internet of Things and social network services become commonplace. As the data grows exponentially, there is a growing demand for services that recommend the information that users want from large amounts of data. Collaborative filtering method is commonly used in information recommendation methods. One of the problems with collaborative filtering-based recommendation method is the cold start problem. In this paper, we propose a method to improve the cold start problem. That is, it solves the cold start problem by mapping the item evaluation data that does not exist to the initial user to the automatically generated data from the mobile app. We describe the main contents of the proposed method and explain the proposed method through the book recommendation scenario. We show the superiority of the proposed method through comparison with existing methods.

Keywords

References

  1. 박선우, "빅데이터 시대와 데이터 융합," 정보통신 정책연구원, 제30권 제1호, 2018, pp.1-24.
  2. 손지은, 김성범, 김현중, 조성준, "추천 시스템 기법 연구동향 분석," 대한산업공학회지, 제41권, 제2호, 2015, pp.185-208. https://doi.org/10.7232/JKIIE.2015.41.2.185
  3. J. Jiguang, Y. Xueli, S.Jingyu "TDCF: Time Distribution Collaborative Filtering Algorithm", International Symposium on Information Science and Engineering, Vol. 1, 2008, pp.98-101.
  4. Pesenson, M.Z., Pesenson, I.Z., McCollum, B., "The data big bang and the expanding digital universe: high-dimensional, complex and massive data sets in an inflationary epoch," Adv. Astron. 2010, pp.1-16. http://dx.doi.org/10.1155/2010/350891.
  5. Trabucchi, D., Buganza, T., Dell'Era, C. and Pellizzoni, E., "Exploring the inbound and outbound strategies enabled by user generated Big Data: evidence from leading smartphone applications," Creativity and Innovation Management, Vol. 27 No. 1, 2018, pp. 42-55. https://doi.org/10.1111/caim.12241
  6. Sarwar, Badrul; Karypis, George; Konstan, Joseph; Riedl, John, "Item-based collaborative filtering recommendation algorithms," Proceedings of the 10th international conference on the World Wide Web (ACM), 2001, pp.285-295.
  7. 권준희, 김성림, "사물인터넷 환경에서 새로운 사용자를 고려한 정보 추천 기법," 디지털산업정보학회 논문지, 제13권 제1호, 2017,pp.23-35.
  8. 김성림, 권준희, "사물인터넷 환경에서 소셜 네트워크를 기반으로 한 정보 추천 기법," 디지털산업정보학회 논문지, 제11권 제1호, 2015, pp.47-56.
  9. https://www.netflix.com/kr/
  10. https://www.amazon.com/
  11. https://watcha.com/
  12. 김동현, 박찬영, 오진오, 유환조, "추천시스템에서의 Cold-Start 문제에 관한 연구 동향," 정보과학회지 제34권, 제6호, 2016, pp.16-21.
  13. 박종학, 조윤호, 김재결, "사회연결망 : 신규고객 추천문제의 새로운 접근법," 지능정보연구 제15권 제1호, 2009, pp.123-140.
  14. 이홍주, 김종우, 박성주, "협업 필터링 기반 상품 추천에서의 평가 횟수와 성능," 한국경영과학회지 제31권 제2호, 2006, pp.27-39.
  15. 김용, 김문석, 김윤범, 박재홍, "이용자 이용행위 및 콘텐츠 위치정보에 기반한 개인화 추천방법에 관한 연구," 정보관리학회지, 제26권, 제1호, 2009, pp.81-105. https://doi.org/10.3743/KOSIM.2009.26.1.081
  16. Sarwar, B., G. Karypis, J. A. Konstan, and J. Riedl, "Analysis of recommendation algorithms for e-commerce," Proceeding EC '00 Proceedings of the 2nd ACM conference on Electronic commerce, 2000, pp.158-167.
  17. Krulwich, B., "Lifestyle Finder : Intelligent User Profiling Using Large-Scale Demographic Data," Artificial Intelligence Magazine, Vol.18, No.2, 1997, pp.37-45.
  18. Schein, A. I., A. Popescul, D. M. Pennock, and L. H. Ungar, "Methods and Metrics for Cold-Start Recommendations," SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002, pp.253-260.
  19. Dae Hoon Park, Mengwen Liu, ChengXiang Zhai, Haohong Wang, "Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval," Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 533-542.
  20. Ning Chen, Steven C. H. Hoiy, Shaohua Liz, Xiaokui Xiao, "Mobile App Tagging," Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016, pp.63-72.
  21. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze, "Introduction to Information Retrieval," Cambridge University Press. 2008.
  22. Jun Wang, Arjen P. de Vries, Marcel J.T. Reinders, "Unifying user-based and item-based collaborative filtering approaches by similarity fusion," Proceeding SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp.501-508.