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Development of User Decision Support System for Leisure Kayak Model Design

레저용 카약 디자인 설계를 위한 사용자 의사결정 지원 시스템 개발

  • Received : 2014.02.26
  • Accepted : 2014.04.14
  • Published : 2014.04.30

Abstract

The change from people's work-centered values to their leisure-centered values leads into the change in their life styles. In the circumstance, people's participation into sports activities seems to be an important means to improve their quality of life. Along with the change of the times, more people take part in water leisure sports including kayak. As a result, people's needs for various designs of water leisure goods are on the rise. In this sense, it is necessary to come up with strategies to actively respond to such a change. In this paper, we proposed a user decision-making support system for designing kayaks for leisure. Based on the previous studies and literatures and a questionnaire survey with consumers, it chose the sensitivity related to design. By conducting factor analysis and evaluation, it drew sensitivity and proposed kayak design layouts in the aspect of customer sensitivity preference. It is expected that the result of this study will be used not only for kayak design, but as a design guide for the equipment of water leisure sports, and will be applied for user-friendly design.

Keywords

Kayak;Leisure Sports;Recommender Systems;Collaborative Filtering;Similarity Weight

References

  1. H. J. Na, B. S. Kim, and H. R. Kim, "The Effects of Selection Attributes, Service Value, Satisfaction and Behavioral Intentions for Water Leisure Sports", Korean Society of Safety Education, Vol. 8, No. 1, pp. 31-53, pp. 745-746, 2012.
  2. K. Y. Kim, D. K. Kim, "The Relationship between Relationship Marketing Implement Factors and Customer Loyalty of Water Leisure Sport Facilities", Korean Society for Sport Management, Vol. 13, No. 1, pp. 41-55, 2008.
  3. J. B. Schafer, J. Konstan, and J.Riedi, "Recommender Systems in e-Commerce", In Proc of the 1st ACM conference on Electric commerce, Vol. 1, pp. 158-166. 1999.
  4. H. N. Kim, A. T. Jia, I. A. Ha, and G. S. Joa, "Collabor ative Filtering based on Collaborative Tagging for Enhancing the Quality of Recommendation", Journal of Electronic Commerce Research and Applications, Vol.9, Issue 1, pp.73-83, 2010. https://doi.org/10.1016/j.elerap.2009.08.004
  5. K. Y. Jung, J. H. Lee, "User Preference Mining through Hybrid Collaborative Filtering and Content-based Filtering in Recommendation System", IEICE Transaction on Information and Systems, Vol. E87-D, N o.12, pp. 2781-2790, 2004.
  6. J. S. Breese, D. Heckerman, C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering", In Proceedings of the Fourteenth Annual Conferenceon Uncertaintyin Artificial Intelligence, Vol. 1, pp. 43-52,1998.
  7. M. Tang, Y. Jiang, J. Liu, and X. Liu, "Location-Aware Collaborative Filtering for QoS-Based Service Recommendation", In Proc. of the IEEE 19th Conference on Web Services, Vol. 1, pp. 202-209, 2012.
  8. H. Tan, H. Ye, "A Collaborative Filtering Recommen dation Algorithm Based on Item Classification", In Proc. of the Pacific-Asia Conference on Circuits, Communications and Systems, Vol. 1, pp. 694-697, 2009.
  9. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes and M. Sartin. "Combining Content-based and Collaborative Filters in an Online Newspaper", In the Proc. of ACM SIGIR Workshop on Recommen der Systems: Algorithms and Evaluation, Vol. 1, pp. 241-250, 1999.
  10. M. J. Pazzani, "A Framework for Collaborative, Content-based and Demographic Filtering", Journal of Artificial Intelligence Review, Vol. 13, No. 5, pp. 393-408, 1999. https://doi.org/10.1023/A:1006544522159
  11. H. Draschsler, H. Hummel, R. Koper, "Personal Recommender System for Learners on Lifelong Learning Networks: The Requirements, Techniques and Model", International Journal Learning Technology, Vol. 3, No. 4, pp. 404-423, 2008. https://doi.org/10.1504/IJLT.2008.019376
  12. Q. Liu, E. Chen, and H. Xiong, "Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking", IEEETransactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42, Issue 1, pp. 218-233, 2012. https://doi.org/10.1109/TSMCB.2011.2163711
  13. D. Billsus, M. J. Pazzani, "Learning Collaborative Information Filters", Proc. of International Conference on Machine Learning, Vol. 1, pp.46-53, 1998.
  14. R. Agrawal, R. Srikant, "Fast Algorithms for Mining Association Rules", Proc. of the 20th VLDB Conf. Vol. 1, pp. 101-107, 1994.
  15. S. J. Gong, G. H. Cheng, "Mining User Interest Change for Improving Collaborative Filtering", In Proc. of the Second International Symposium on Intelligent Information Technology Application, Vol. 1, pp. 24-27, 2008.
  16. Z. Yang, G. A. Levow, and H. Meng, "Predicting User Satisfaction in Spoken Dialog System Evaluation With Collaborative Filtering", IEEE Journal of Selected Topics in Signal Processing, Vol. 6, Issue 8, pp. 971-981, 2012. https://doi.org/10.1109/JSTSP.2012.2229965
  17. L. Mendo, "Estimation of a Probability with Guaranteed Normalized Mean Absolute Error", IEEE Communications Letters, Vol. 13, Issue 11, pp. 817-819, 2009. https://doi.org/10.1109/LCOMM.2009.091128
  18. H. I. Kim, "App Recommendation Based on Characteristic Similarity", Digital Contents Society, Vol. 13, No. 4, pp. 559-565, 2012. https://doi.org/10.9728/dcs.2012.13.4.559
  19. E. E. Jo, C. J. Moon, D. H. Park, "A Categorization Method based on RCBAC for Enhanced Contents and Social Networking Service for User", Digital Contents Society, Vol. 13, No. 1, pp. 101-110, 2012. https://doi.org/10.9728/dcs.2012.13.1.101

Acknowledgement

Grant : 스포츠과학기반 다기능 소형 포터블 보트 및 IT융합 라이프 자켓 개발

Supported by : 문화체육관광부