Advertisement

A Flexible Competitive Neural Network for Eliciting User’s Preferences in Web Urban Spaces

  • Yanwu Yang
  • Christophe Claramunt

Abstract

Preference elicitation is a non-deterministic process that involves many intuitive and non well-defined criteria that are difficult to model. This paper introduces a novel approach that combines image schemata, affordance concepts and neural network for the elicitation of user’s preferences within a web urban space. The selection parts of the neural network algorithms are achieved by a web-based interface that exhibits image schemata of some places of interest. A neural network is encoded and decoded using a combination of semantic and spatial criteria. The semantic descriptions of the places of interest are defined by degrees of membership to predefined classes. The spatial component considers contextual distances between places and reference locations. Reference locations are possible locations from where the user can act in the city. The decoding part of the neural network algorithms ranks and evaluates reference locations according to user’s preferences. The approach is illustrated by a web-based interface applied to the city of Kyot

Keywords

image schemata preference elicitation competitive neural network web GIS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brown B and Chalmers M (2003) Tourism and mobile technology. In: Proceedings of the 8th European Conference on Computer Supported Cooperative Work, 14th–18th September, Helsinki, Finland, Kluwer Academic Publishers.Google Scholar
  2. Chiclana F, Herrera F and Herrera-Viedma E (1998) Integrating three representation models in multipurpose decision making based on preference relations. Fuzzy Sets and Systems, 97: 33–48.CrossRefGoogle Scholar
  3. Freeman JA and Skapura DM (1991) Neural Networks: Algorithms, Applications and Programming Techniques, Addison-Wesley, MA.Google Scholar
  4. Gibson J (1979) The Ecological Approach to Visual Perception. Houghton Mifflin Company, Boston.Google Scholar
  5. Greco G, Greco S and Zumpano E (2001) A probabilistic approach for distillation and ranking of web pages. World Wide Web: Internet and Information Systems, 4(3): 189–208.Google Scholar
  6. Haddawy P, Ha V, Restificar A, Geisler B and Miyamoto J (2003) Preference elicitation via theory refinement. Journal of Machine Learning Research, 4: 317–337.Google Scholar
  7. Johnson M (1987) The Body in the Mind: The Bodily Basis of Meaning, Imagination, and Reason. The University of Chicago Press, Chicago.Google Scholar
  8. Kacprzyk, J (1986) Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems, 18: 105–118.Google Scholar
  9. Kleinberg JL (1999) Authoritative sources in an hyperlinked environment. Journal of the ACM, 46(5):604–632.CrossRefGoogle Scholar
  10. Kuhn W (1996) Handling Data Spatially: Spatializing User Interfaces. In: Kraak MJ and Molenaar M (Eds.), SDH’96, Advances in GIS Research II, Proceedings. 2, International Geographical Union, Delf, pp 13B.1–13B.23.Google Scholar
  11. Linden G, Hanks S and Lesh N (1997) Interactive assessment of user preference models: The automated travel assistant. User Modeling, June.Google Scholar
  12. Madria SK, Bhowmick SS, Ng WK and Lim EP (1999) Research issues in web data mining. In: Proceedings of the 1st International Conference on Data Warehousing and Knowledge Discovery, pp. 303–312.Google Scholar
  13. Riecken (2000) Personalized views of personalization. Communications of the ACM, 43(8): 26–29.Google Scholar
  14. Saaty TL (1980) The Analytic Hierarchy Process, McGraw-Hill, New-York.Google Scholar
  15. Schafer JB, Konstan J and Riedl J (1999) Recommender systems in ecommerce. In: Proceedings of the ACM Conference on Electronic Commerce, pp. 158–166.Google Scholar
  16. Shavlik J and Towell G (1989) An approach to combining explanation-based and neural learning algorithms, Connection Science, 1(3): 233–255.Google Scholar
  17. Shearin S and Lieberman H (2001) Intelligent profiling by example. In: Proceedings of the International Conference on Intelligent User Interfaces (IUI 2001), Santa Fe, NM, pp. 145–152.Google Scholar
  18. Tezuka T, Lee R, Takakura H and Kambayashi Y (2001) Web-based inference rules for processing conceptual geographical relationships. In: Proceedings of the 1st IEEE International Web GIS Workshop, pp. 14–24.Google Scholar
  19. Thorndyke PW and Hayes-Roth B (1980) Differences in Spatial Knowledge Acquired from Maps and Navigation, Technical Report N-1595-ONR, The Office of Naval Research, Santa Monica, CA.Google Scholar
  20. Worboys M (1996) Metrics and topologies for geographic space. In: Kraak MJ and Molenaar M (Eds.), SDH’96, Advances in GIS Research II, Proceedings. 2,International Geographical Union, Delf, pp. 365–375.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yanwu Yang
    • 1
  • Christophe Claramunt
    • 1
  1. 1.Naval Academy Research InstituteBrest NavalFrance

Personalised recommendations