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Preference-based Clustering for Intelligent Shared Environments

공용환경 설계를 위한 선호도 기반 클러스터링

  • Received : 2013.01.26
  • Accepted : 2013.02.18
  • Published : 2013.03.31

Abstract

In ubiquitous computing, shared environments adjust themselves so that all users in the environments are satisfied as possible. Inevitably, some of users sacrifice their satisfactions while the shared environments maximize the sum of all users' satisfactions. In our previous work, we have proposed social welfare functions to avoid a situation which some users in the system face the worst setting of environments. In this work, we consider a more direct approach which is a preference based clustering to handle this issue. In this approach, first, we categorize all users into several subgroups in which users have similar tastes to environmental parameters based on their preference information. Second, we assign the subgroups into different time or space of the shared environments. Finally, each shared environments can be adjusted to maximize satisfactions of each subgroup and consequently the optimal of overall system can be achieved. We demonstrate the effectiveness of our approach with a numerical analysis.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

References

  1. Baek, D. and Jin, H., A Feasibility Study on the Location Based Services under Ubiquitous Environment. Journal of the Society of Korea Industrial and Systems Engineering, 2007, Vol. 30, No. 3, p 1-11.
  2. Diaconis, P. and Graham, R.L., Spearmanʼs footrule as a measure of disarray. Journal of the Royal Statistical Society, Series B (Methodological), 1977, Vol. 39, No. 2, p 262-268.
  3. Ha, V. and Haddawy, P., Toward Case-Based Preference Elicitation: Similarity Measure on Preference Structures. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madiason, WI 1998, p 193-201.
  4. Kendall, M.G. and Gibbons, J.D., Rank Correlation Methods(5th ed). New York : Oxford University Press, 1990.
  5. Lee, S. and Ok, C.-S., Group Preference Aggregate Functions Based on Social Welfare : Beyond Sum of Satisfactions, 2013, Information Sciences, Under revision.
  6. Masthoff, J., The Pursuit of Satisfaction : Affective State in Group Recommender Systems. LNAI 3538, 2005, Vol. 3538, p 297-306.
  7. McCarthy, J.F. and Anagnost, T.D., MusicFX : An arbiter of group preferences for computer supported collaborative workouts. Proc. ACM 1998 Conference on Computer Supported Cooperative Work, 1998, p 363-372.
  8. Nam, J., Kim, H., Shin, D., Park, J., and Hur, S., A Formal Model of Coordination for Supporting Community Computing in a Ubiquitous Environment. Journal of the Society of Korea Industrial and Systems Engineering, 2008, Vol. 31, No. 3, p 43-51.
  9. Ok, C.-S., Lee, S., and Jeong, B., Toward socially agreeable aggregate functions for group recommender systems. The Korean Operations Research and Management Science Society, 2007, Vol. 32, No. 4, p 61-75.
  10. Rohatgi, V.K., An Introduction to Probability Theory and Mathematical Statistics. John Wiley and Sons, 1796.
  11. Russell, D., Streitz, N., and Winograd, T., Building disappearing computers. Communications of the ACM. 2005, Vol. 48, No. 3, p 42-48.
  12. Tandler, P., Streitz, N., and Prante, T., Roomware-Moving toward ubiquitous computers. IEEE Micro, 2002, Vol. 22, No. 6, p 36-47.
  13. Weiser, M., The computer for the 21st century. Scientific American, 1991, Vol. 265, No. 3, p 94-104.