대한산업공학회지 (Journal of Korean Institute of Industrial Engineers)

  • 제33권4호
  • /
  • Pages.402-409
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  • 2007
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  • 1225-0988(pISSN)
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  • 2234-6457(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지

Markov Chain을 이용한 핸드폰 메뉴 선택 예측

Prediction of Mobile Phone Menu Selection with Markov Chains

  • 이석원 (고려대학교 정보경영공학부) ;
  • 명노해 (고려대학교 정보경영공학부)
  • Lee, Suk Won (Division of Information Management Engineering, Korea University) ;
  • Myung, Rohae (Division of Information Management Engineering, Korea University)
  • 발행 : 2007.12.31
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초록

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

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참고문헌

  1. Anderson, J. R. (2000), Congnitive Psychology and its Implications, 5th Edition, Worth Publishers, New York
  2. Branch, P., Egan, G., and Tonkin, B. (1999), Modeling Interactive Behaviour of a Video Based Multimedia System, IEEE International Conference on Communications, Vancouver, BC, Canada, vol. 2, 978-982
  3. Bridle, R. and McCreath, E. (2005), Predictive Menu Selection on a Mobile Phone, Proceedings of the Workshop W7 on Mining Spatio-Temporal Data, ECML/PKDD, Porto, Portugal, 1328-1341
  4. Dongshan, X. and Junyi, S. (2002), A New Markov Model for Web Access Prediction, IEEE Computing in Science and Engineering, 4(6), 34-39 https://doi.org/10.1109/MCISE.2002.1046594
  5. Hillier, F. S. and Lieberman, G. J. (2001), Introduction to Operations Research, 7th Edition, McGraw-Hill, Boston
  6. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K., (1998), The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, 256-265
  7. Kigima, M. (1997), Markov Processes for Stochastic Modeling, Chapman & Hall, London
  8. Kumagai, T. and Akamatsu, M. (2004), Modeling and Prediction of Driving Behavior, Proceeding of IMEKO/IEEE/SICE 2nd International Symposium on Measurement, Analysis, and Modeling of Human Functions, Genoa, Italy, 357-361
  9. Lui, J., Wong, C. K., and Hui, K. K. (2003), An Adaptive User Interface Based on Personalized Learning, IEEE Intelligent Systems, 18(2), 52-57
  10. McGrenere, J. (2004), Multiple User Interfaces, John Wiley & Sons, Ltd, England
  11. Miller, J. R., Sullivan, J. W., and Tyler, S.W. (1991), Intelligent User Interfaces, ACM Press, New York
  12. Moriyon, R., Szekely, P., and Neches, R. (1994), Automatic Generation of Help From Interface Design Models, Conference companion on Human factors in computing systems, Boston, Massachusetts, 225-231
  13. Sarukkai, R. R. (2000), Link Prediction and Path Analysis Using Markov Chains, Proceedings of the 9th International World Wide Web Conference on Computer Networks: The International Journal of Computer and Telecommunications Networking, Amsterdam, Netherlands, 377-386
  14. Shneiderman, B. and Plaisant, C. (2005), Designing The User Interface: Strategies for Effective Human-Computer Interaction, 4th Edition, Addison Wesley, Boston
  15. Spears, W. M. (1999), A Compression Algorithm for Probability Transition Matrices, SIAM Journal on Matrix Analysis and Applications, 20(1), 60-77 https://doi.org/10.1137/S0895479897316916
  16. Stipp, H. (1999), Convergence now?, International Journal on Medial Management, 1, 10-13 https://doi.org/10.1080/14241279909384481
  17. Stuerzlinger, W., Chapuis, O., Phillips, D., and Roussel, N. (2006), User Interface Facades : Towards Fully Adaptable User Interfaces, Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, Montreux, Switzerland, 309- 318
  18. Wanda, F. and Tano, S. (2000), Basic System Architecture for Adaptive Information Presentation in High Cognitive Load Environment, 26th Annual Conference of the IEEE Industrial Electronics Society, Nagoya, Japan, 1, 515-520
  19. Zhu, J., Hong, J., and Honhes, J. G.(2002), Using Markov Chains for Link Prediction in Adaptive Web Sites, In Proceeding of Soft-Ware 2002 : First International Conference on Computing in an Imperfect World, Springer, Belfast, Northern Ireland, 60-73, Lecture Notes in Computer Science