Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Recommendation System Based-on Interactive Evolutionary Computation with Data Grouping

데이터 그룹화를 이용한 상호진화연산 기반의 추천 시스템

  • 김현태 (성균관대학교 정보통신공학부) ;
  • 안창욱 (성균관대학교 정보통신공학부) ;
  • 안진웅 (대구경북과학기술원)
  • Received : 2011.05.20
  • Accepted : 2011.06.20
  • Published : 2011.08.01
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Abstract

Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user's preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user's preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.

Keywords

References

  1. P. Resnick and H. R. Varian, "Recommender system," Communications of the ACM, vol. 40, no. 3, pp. 56-58, 1997. https://doi.org/10.1145/245108.245121
  2. L. Terveen and W. Hill, "Beyond recommender systems: helping people help each other," HCI in the New Millennium, Addison-Wesley, pp. 487-509, 2001.
  3. J. Schafer, J. Konstan, and J. Riedl, "Recommender systems in E-commerce," Proc. of the 1st ACM conference on Electronic commerce, pp. 158-166, 1999.
  4. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Analysis of recommendation algorithms for e-commerce," Proc. of the 2nd ACM conference on Electronic commerce, pp. 158-167, Oct. 2000.
  5. M. Balabanovi'c and Y. Shoham, "FAB: content-based, collaborative recommendation," Communications of the ACM, vol. 40, no. 3, pp. 66-72, 1997. https://doi.org/10.1145/245108.245124
  6. R. Burke, "Hybrid web recommender systems," Lecture Notes in Computer Science, vol. 4321, pp. 377-408, 2007. https://doi.org/10.1007/978-3-540-72079-9_12
  7. G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, pp. 734-749, June 2005. https://doi.org/10.1109/TKDE.2005.99
  8. M. J. Pazzani, "A framework for collaborative, content-based and demographic filtering," Artificial Intelligence Review, vol. 13, no. 5-6, pp. 394-408, 1999.
  9. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: an open architecture for collaborative filtering of netnews," Proc. of the Conference on Supported Cooperative Work Conf., pp. 175-186, 1994.
  10. D. Goldberg, D. Nichols, B. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communications of the ACM, 35, 12 1992.
  11. K. B. Sim and K. E. Ko, "Study of emotion recognition based on facial image for emotional rehabilitation biofeedback," Journal of Institute of Control,Robotics and Systems (in Korean), vol. 16, no. 10, pp. 957-962, Oct. 2010. https://doi.org/10.5302/J.ICROS.2010.16.10.957
  12. D. W. Lee, K. R. Cho, and S. W. Baek, "Fitness change of mission scheduling algorithm using genetic theory according to the control constants," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 16, no. 6, pp. 572-578, June 2010. https://doi.org/10.5302/J.ICROS.2010.16.6.572
  13. D. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, IEEE Press, 1996
  14. M. Mitchell, an Introduction to Genetic Algorithm, MIT Press, 1998.
  15. D. E. Goldberg and J. H. Holland, "Genetic algorithms and machine learning," Machine Learning, vol. 3, no. 2-3, pp. 95-99, 1988. https://doi.org/10.1007/BF00113892
  16. H. Takagi, "Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation," Proc. of the IEEE, vol. 89, pp. 1275-1296, 2001. https://doi.org/10.1109/5.949485
  17. H. T. Kim, E. G. Kim, J. H. Lee, and C. W. Ahn, "A recommender system based on genetic algorithm for music data," Proc. of International Conference of Computer Engineering and Technology, vol. 6, pp. 414-417, 2010.
  18. M. David, "Chapter 20. an example inference task: clustering," Information Theory, Inference and Learning Algorithm, 2003.
  19. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, "Constrained k-means clustering with background Knowledge," Proc. of 18th International Conference on Machine Learning, pp. 577-584, 2001.
  20. D. Thierens and D. Goldberg, "Elitist recombination: an integrated selection recombination GA," Proc. of the First IEEE Conference on Evolutionary Computation, pp. 508-512, 1994. https://doi.org/10.1109/ICEC.1994.349898
  21. J. F. Crow and M. Kimura, "Efficiency of truncation selection," Proc. of the National Academy of Sciences of the United States of America, vol. 76, no. 1, pp. 396-399, 1979. https://doi.org/10.1073/pnas.76.1.396
  22. F. Herrera, M. Lozano, and J. L. Verdegay, "Tackling real-coded genetic algorithms: operators and tools for behavioural analysis," Artificial Intelligence Review, vol. 12, pp. 265-319, 1998. https://doi.org/10.1023/A:1006504901164
  23. CLAM, http://clam-project.org