Automatically improving the accuracy of user profile with genetic algorithm
Inhaltsverzeichnis
Referenz
Y. Chen, C. Shahabi: Automatically improving the accuracy of user profile with genetic algorithm. in: Proc. International Conference on Artificial Intelligence and Soft Computing, Cancun, Mexico, 2001
DOI
Abstract
With information retrieval systems, bridging the gap between the physical characteristics of data with the user perceptions is challenging. In order to address this challenge, employing user profiles to improve the retrieval accuracy becomes essential. However, the system performance may degrade due to inaccuracy of user profiles. Therefore, for an approach to be effective, it should offer a learning mechanism to correct user input errors. Focusing on an image retrieval application, we utilize the users’ relevance feedback to improve the profiles automatically using genetic algorithms (GA). Our experimental results indicated that the retrieval accuracy is significantly increased using the GA-based learning mechanism.
Extended Abstract
Bibtex
@incollection{
year={2001},
isbn={},
booktitle={Proc. International Conference on Artificial Intelligence and Soft Computing, Cancun, Mexico},
volume={},
series={},
editor={},
doi={},
title={Automatically improving the accuracy of user profile with genetic algorithm},
url={http://infolab.usc.edu/DocsDemos/asc01.pdf http://de.evo-art.org/index.php?title=Automatically_improving_the_accuracy_of_user_profile_with_genetic_algorithm},
publisher={},
keywords={},
author={Y. Chen, C. Shahabi},
pages={},
language={English}
}
Used References
[1] M. Balabanovi. An adaptive web page recommendation service. In Proceedings of Autonomous Agents, pages 378– 385, Marina Del Rey, California USA, 1997.
[2] A. Berger and J. Lafferty. Information retrieval as statistical translation. In Proceedingsof the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, Berkeley, CA USA, August 1999.
[3] Y.-S. Chen and J.-S. Chen. A natural genetic algorithm in job shop problem. Master Thesis, National Centrail University, Taiwan, 1997.
[4] R. Fagin. Combining fuzzy information from multiple systems. In Proc. Fifteenth ACM Symp. on Principles of Database Systems, pages 216–226, 1996.
[5] R. Fagin. Fuzzy queries in multimedia database systems. ACM SIGACT-SIGMOD-SIGART Symposium on Principle of Database Systems, 1998.
[6] D. Goldberg. Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley,Wokingham, England, 1989.
[7] V. Gudivada and V. Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. IEEE Transations on Information Systems, 13(2):115–144, 1995.
[8] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack. Efficient color histogram indexing for quadratic form distance functions. IEEE Trans on Pattern Analysis and Machine Intelligence, 17:729–736, 1995.
[9] J. Holland. Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan, 1975. [10] A. Hunter. Sugal programming manual. http://www.trajansoftware. demon.co.uk/sugal.htm, 1995.
[11] N. Karnik and J. Mendel. Introduction to type-2 fuzzy logic systems. In Proceeding of 1998 IEEE FUZZ Conference, pages 915–920, Anchorage, AK, May 1998.
[12] N. Karnik and J. Mendel. Operations on type-2 fuzzy sets. Int’l. J. on Fuzzy Sets and Systems, 2000.
[13] W. Lam, S. Mukhopadhyay, J. Mostafa, and M. Palakal. Detection of shifts in user interests for personalized information filtering. In Proceeding of the 19th Int’l ACMSIGIR Conf on Research and Development in Information Retrieval, pages 317–325, 1996.
[14] P. Maes. Agents that reduce work and information overload. Communications of the ACM, 37(7):30–40, 1994.
[15] M. Mirmehdi and M. Petrou. Segmentation of color textures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:142–159, 2000.
[16] A. Moukas. Amalthea: Information discovery and filtering using a multiagent evolving ecosystem. In 1st Int. Conf. on The Practical Applications of Intelligent Agents and Multi- Agent Technology (PAAM), London, 1996.
[17] M. Pazzani and D. Billsus. Learning and revising user profiles: The indentification of interesting web sites. Machine Learning, 27:313–331, 1997.
[18] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM Conf. on Computer-SupportedCooperativeWork, pages 175–186, Chapel Hill, NC, 1994.
[19] C. Shahabi and Y.-S. Chen. Efficient support of soft query in image retrieval systems. In Proceeding of SSGRR 2000 Computer and eBusiness Conference, Rome, Italy, August 2000.
[20] C. Shahabi and Y.-S. Chen. Soft query in image retrieval systems. In Proceeding of SPIE Internet Imaging , Electronic Imaging 2000, volume 3964, pages 57–68, San Jose, CA, 2000.
[21] C. Shahabi and Y.-S. Chen. A unified and efficient framework for customized query in image retrieval systems. In Proceeding of International Conference on Enterprise Information Systems, Setubal, Portugal, July 2001.
[22] B. Sheth. Evolving agents for personalized information filtering. In Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications, 1993.
[23] A. Tan and C. Teo. Learning user profiles for personalized information dissemination. In Proceeding of Int’l Joint Conf. on Neural Network, pages 183–188, 1998.
[24] Y. Theodoridis, E. Stefanakis, and T. Sellis. Efficient cost models for spatial queries using r-trees. IEEE Transactions on Knowledge and Data Engineering, 12(1):19–32, 2000.
[25] D. Widyantoro, T. Ioerger, and J. Yen. An adaptive algorithm for learning changes in user interests. In 8th Int’l Conf on Information and Knowledge Management,November 1999.
[26] L. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1(1), pages 3–28, 1978.
Links
Full Text
http://infolab.usc.edu/DocsDemos/asc01.pdf
