DOI QR코드

DOI QR Code

기계 학습 방법을 이용한 활동 프로파일 기반의 스마트 시니어 분류 모델 개발

Development of Smart Senior Classification Model based on Activity Profile Using Machine Learning Method

  • 윤유동 (고려대학교 정보대학 컴퓨터학과) ;
  • 양영욱 (고려대학교 정보대학 컴퓨터학과) ;
  • 지혜성 (고려대학교 정보대학 컴퓨터학과) ;
  • 임희석 (고려대학교 정보대학 컴퓨터학과)
  • Yun, You-Dong (Dept. of Computer Science and Engineering, Korea University) ;
  • Yang, Yeong-Wook (Dept. of Computer Science and Engineering, Korea University) ;
  • Ji, Hye-Sung (Dept. of Computer Science and Engineering, Korea University) ;
  • Lim, Heui-Seok (Dept. of Computer Science and Engineering, Korea University)
  • 투고 : 2016.11.28
  • 심사 : 2017.01.20
  • 발행 : 2017.01.28

초록

최근 스마트폰의 보급 및 웹 서비스의 도입으로 온라인 사용자들은 대규모의 콘텐츠를 시간과 장소에 관계없이 접할 수 있게 되었다. 그러나 사용자들은 대규모의 콘텐츠 사이에서 원하는 콘텐츠를 찾는 데 어려움을 겪게 되었다. 이러한 문제를 해결하기 위해 다양한 분야에서 사용자 모델링 및 추천 시스템에 대한 연구가 활발하게 수행되었다. 그러나 정보 환경의 변화에 따른 시니어 계층의 적극적인 변화에도 불구하고 시니어 계층에 초점을 맞춘 사용자 모델링 및 추천 시스템에 대한 연구는 매우 부족한 실정이다. 이에 본 논문에서는 기계 학습 방법을 기반으로 스마트 시니어 계층의 선호도를 파악할 수 있는 모델링 방법을 제안하고, 스마트 시니어 분류 모델을 개발한다. 이 결과, 스마트 시니어 계층의 선호도를 파악할 수 있을 뿐만 아니라 스마트 시니어 분류 모델 개발을 통해 시니어 사용자에게 가장 적합한 활동 및 콘텐츠를 제공하는 콘텐츠 추천 연구에 대한 발판을 마련하였다.

With the recent spread of smartphones and the introduction of web services, online users can access large-scale content regardless of time or place. However, users have had trouble finding the content they wanted among large-scale content. To solve this problem, user modeling and content recommendation system have been actively studied in various fields. However, in spite of active changes in senior groups according to the changes in information environment, research on user modeling and content recommendation system focused on senior groups are insufficient. In this paper, we propose a method of modeling smart senior based on their preference, and further develop a smart senior classification model using machine learning methods. As a result, we can not only grasp the preferences of smart seniors, but also develop a smart senior classification model, which is the foundation for the research of a recommendation system which will provide the activities and contents most suitable for senior groups.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. M. J. Kim, D. S. Park, M. Hong, H. M. Lee, "Personalized Movie Recommendation System Using Context-Aware Collaborative Filtering Technique", KIPS Transactions on Computer and Communication Systems, Vol. 4, No. 9, pp. 289-296, 2015. https://doi.org/10.3745/KTCCS.2015.4.9.289
  2. J. C. Hung, J. D. Weng, Y. H. Chen, "A recommendation system based on mining human portfolio for museum navigation", Evolving Systems, Vol. 7, No. 2, pp. 145-158, 2016. https://doi.org/10.1007/s12530-016-9154-8
  3. D. M. Lee, J. G. Kim, "Study on UX Design Direction of New Residential Space according to the Change of the Elderly's Life Style", Journal of Korea Design Knowledge, Vol. 31, pp. 135-144, 2014.
  4. H. K. Ko, "Affordance Planning Strategy for Mathematics App development for Senior citizen using Smart-devices", Communications of mathematical education, Vol. 30, No. 1, pp. 85-99, 2016. https://doi.org/10.7468/jksmee.2016.30.1.85
  5. Zheng, X., & Pulli, P., "Towards high quality mobile services for Senior citizens in smart living environments", In 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), pp. 164-170, 2007.
  6. Massimi, M., Baecker, R. M., & Wu, M., "Using participatory activities with seniors to critique, build, and evaluate mobile phones", In Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility, pp. 155-162, 2007.
  7. A. R. Beak, S. J. Jun, "A Research on Emotional Design for Mobile Phone in Next Generation", Journal of Integrated Design Research, Vol. 14, No. 4 pp. 21-34, 2015.
  8. H. S. Kim, "A Study on the Usage of Mobile Music Application in Senior Group: Focused on Smart Phones for Active Senior", Journal of the Korean Society of Design Culture, Vol. 18, No. 1 pp. 103-116, 2015.
  9. S. I. Jeong, "A Study on Smarthome APP GUI for the Active Senior", Journal of Korean Institute of Culture Product, Vol. 44, pp. 83-92, 2016.
  10. B. K. Lee, S. H. Kim, "A Study on UX/UI of Healthcare Based Application Contents for Active Seniors", JJournal of the Korean Society of Design Culture, Vol. 21, No. 4 pp. 433-445, 2015.
  11. B. J. Lee, J. S. Kwon, G. C. Go, Y. L. Choi, "A Method for Analyzing Web Log of the Hadoop System for Analyzing a Effective Pattern of Web Users", Journal of the Korea society of IT services, Vol. 13, No. 4, pp. 231-243, 2014. https://doi.org/10.9716/KITS.2014.13.4.231
  12. S. I. Choi, N. G. Kim, "Identifying the Interests of Web Category Visitors Using Topic Analysis", Journal of Information Technology Applications and Management, Vol. 21, No. 4, pp. 415-429, 2014.
  13. H. J. Lee, "An Analysis on Communication Behaviors Performed by Digital Photography Community Users - focusing on the Photo Gallery Service, NAVER", Yonsei University Graduate School of Journalism and Broadcasting Master's Thesis, 2011.
  14. D. C. Lee, E. J. Lee, B. S. Kim, G. O. Jin "A Study of Influencing Factors in Internet Shopping of the Consumer's Purchase Intention", Management Information Systems review, Vol. 30, No. 1, pp. 211-226 2011.
  15. S. H. Jang, "Web site analysis using data mining : around web log analysis", Korea University Graduate School of Computer and Information System Master's Thesis, 2010.
  16. Feng, M., Heffernan, N., & Koedinger, K., "Addressing the assessment challenge with an online system that tutors as it assesses", User Modeling and User-Adapted Interaction, Vol. 19, No. 3, pp. 243-266, 2009. https://doi.org/10.1007/s11257-009-9063-7
  17. Dy, J. G., & Brodley, C. E., "Feature selection for unsupervised learning", Journal of machine learning research, Vol. 5, pp. 845-889, 2004.
  18. G. J. Kim, J. S. Han, "Unsupervised Machine Learning based on Neighborhood Interaction Function for BCI(Brain-Computer Interface)", Journal of digital Convergence, Vol. 13, No. 8 pp. 289-294, 2015. https://doi.org/10.14400/JDC.2015.13.8.289
  19. S. Y. Oh, "Decision Tree State Tying Modeling Using Parameter Estimation of Bayesian Method", Journal of the Korea Convergence Society, Vol. 13, No. 1, pp. 243-248, 2015.
  20. Y. B. Park, G. M. Lee, S. J. Kang, "A Study of Standard Setting Method Using the Cluster Analysis and Validity", User Modeling and User-Adapted Interaction, Vol. 24, No. 3, pp. 645-664, 2011.
  21. Niculescu-Mizil, A., & Caruana, R., "Predicting good probabilities with supervised learning", In Proceedings of the 22nd international conference on Machine learning, pp. 625-632. 2005, ACM.
  22. Islam, M. J., Wu, Q. J., Ahmadi, M., & Sid-Ahmed, M. A., "Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers", In Convergence Information Technology, 2007. International Conference on, pp. 1541-1546, 2007, IEEE.
  23. Pal, M., & Mather, P. M., "An assessment of the effectiveness of decision tree methods for land cover classification", Remote sensing of environment, Vol. 86 No. 4, pp. 554-565, 2003. https://doi.org/10.1016/S0034-4257(03)00132-9
  24. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., & Tourassi, G. D., "Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance", Neural networks, Vol. 21, No. 2, 427-436, 2008. https://doi.org/10.1016/j.neunet.2007.12.031
  25. Saxena, A., & Saad, A., "Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems", Applied Soft Computing, Vol. 7, No. 1, pp. 441-454, 2007. https://doi.org/10.1016/j.asoc.2005.10.001
  26. H. W. Byeon, "The Factors of Participating in a Smoking Cessation Program using Integrated Method of Decision Tree and Neural Network Algorithm", Journal of the Korea Convergence Society, Vol. 6, No. 2, pp. 25-30, 2015. https://doi.org/10.15207/JKCS.2015.6.2.025
  27. S. Y. Choi, H. C. Ahn, "Optimized Bankruptcy Prediction through Combining SVM with Fuzzy Theory", Journal of digital Convergence, Vol. 13, No. 3, pp. 155-165, 2015. https://doi.org/10.14400/JDC.2015.13.3.155
  28. Mavroforakis, M. E., & Theodoridis, S., "A geometric approach to support vector machine (SVM) classification", IEEE transactions on neural networks, Vol. 17, No. 3, pp. 671-682, 2006. https://doi.org/10.1109/TNN.2006.873281
  29. Moreno, P. J., Ho, P. P., & Vasconcelos, N., "A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications", In Advances in neural information processing systems. 2003.