Advertisement

Bayesian Metanetworks for Modelling User Preferences in Mobile Environment

  • Vagan Terziyan
  • Oleksandra Vitko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2821)

Abstract

The problem of profiling and filtering is important particularly for mobile information systems where wireless network traffic and mobile terminal’s size are limited comparing to the Internet access from the PC. Dealing with uncertainty in this area is crucial and many researchers apply various probabilistic models. The main challenge of this paper is the multilevel probabilistic model (the Bayesian Metanetwork), which is an extension of traditional Bayesian networks. The extra level(s) in the Metanetwork is used to select the appropriate substructure from the basic network level based on contextual features from user’s profile (e.g. user’s location). Two models of the Metanetwork are considered: C-Metanetwork for managing conditional dependencies and R-Metanetwork for modelling feature selection. The Bayesian Metanetwork is considered as a useful tool to present the second order uncertainty and therefore to predict mobile user’s preferences.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boutiler, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-Specific Independence in Bayesian Networks. In: Proc. of the 12th Conference on Uncertainty in Artificial Intelligence UAI 1996, pp. 115–123 (1996) Google Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conference on Uncertainty in Artificial Intelligence. Morgan Kauffman, Madison (1998)Google Scholar
  3. 3.
    Butz, C.J.: Exploiting Contextual Independencies in Web Search and User Profiling. In: Proc. of the World Congress on Computational Intelligence, pp. 1051– 1056 (2002) Google Scholar
  4. 4.
    Cadez, I.V., Smyth, P., Mannila, H.: Probabilistic Modeling of Transaction Data with Applications to Profiling, Visualization, and Prediction. In: Proc. of the KDD 2001, pp. 37–46 (2001) Google Scholar
  5. 5.
    Chadha, K.: Location-Based Services: The Next Differentiator. Mobile Internet and Inf. Services (2000), Available in: http://www.the-arc-group.com/ebrief/2000/mobileinternetis/executive_summary.htm
  6. 6.
    Claypool, M., Phong, L., Waseda, M., Brown, D.: Implicit Interest Indicators. In: Proc. of ACM Intelligent User Interfaces Conference IUI 2001, Santa Fe, New Mexico (2001) Google Scholar
  7. 7.
    Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1 (3) (1997) Google Scholar
  8. 8.
    Delgado, J., Ishii, N.: Multi-Agent Learning in Recommender Systems for Information Filtering on the Internet. Int. J. CIS. 10(1-2), 81–100 (2001)Google Scholar
  9. 9.
    Garmash, A.: A geographical XML-based format for the mobile environment. In: Proc. of HICSS Conference, Hawaii (2001) Google Scholar
  10. 10.
    Heckerman, D.: A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research (1995) Google Scholar
  11. 11.
    Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., Kadie, C.: Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Machine Learning Research 1, 49–75 (2000)CrossRefGoogle Scholar
  12. 12.
    Hoffman, T.: Probabilistic latent semantic indexing. In: Proc. of the ACM SIGIR Conference, pp. 50–57. ACM Press, New York (1999)Google Scholar
  13. 13.
    Kuenzer, A., Schlick, C., Ohmann, F., Schmidt, L., Luczak, H.: An empirical study of dynamic Bayesian networks for user modeling. In: Schafer, R., Muller, M.E., Macskassy, S.A. (eds.) Proc. of the UM 2001 Workshop on Machine Learning for User Modeling, pp. 1–10 (2001)Google Scholar
  14. 14.
    Kutschinski, E., Poutre, H.L.: Scientific techniques for interactive profiling. Technical Report (ASTA project). Telematica Instituut, Enschede (2001) Google Scholar
  15. 15.
    Pena, J., Lozano, J.A., Larranaga, P.: Learning Bayesian Networks for Clustering by Means of Constructive Induction. Machine Learning 47(1), 63–90 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Swedberg, G.: Ericsson’s Mobile Location Solution. Ericsson Review (1999) Google Scholar
  17. 17.
    Terziyan, V.: Multilevel Models for Knowledge Bases Control and Automated Information Systems Applications. Doctor of Technical Sciences Degree Thesis. Kharkov State Technical University of Radioelectronics, Kharkov (1993) Google Scholar
  18. 18.
    Terziyan, V., Puuronen, S.: Reasoning with Multilevel Contexts in Semantic Metanetworks. In: Bonzon, P., Cavalcanti, M., Nossun, R. (eds.) Formal Aspects in Context, pp. 107–126. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  19. 19.
    Terziyan, V.: Architecture for Mobile P-Commerce: Multilevel Profiling Framework. In: Proc. of the IJCAI 2001 International Workshop E-Business and the Intelligent Web, Seattle (2001)Google Scholar
  20. 20.
    The MeT Initiative – Enabling Mobile E-Commerce. Met Overview White Paper (2000), Available in: http://www.mobiletransaction.org/pdf/MeT_White_Paper.pdf
  21. 21.
    Veijalainen, J., Terziyan, V.: Transaction Management for M-Commerce at a Mobile Terminal. In: Proc. of the International Workshop on Reliable and Secure Applications in Mobile Environment in conjunction with SRDS 2001, New Orleans (2001)Google Scholar
  22. 22.
    Wassum, B.: Mobile Data Service Models for Mobile Network Operators, Mobile Internet and Inf. Services (2000), Available in: http://www.the-arc-group.com/ebrief/2000/mobileinternetis/executive_summary.htm
  23. 23.
    Wong, S.K.M., Butz, C.J.: A Bayesian Approach to User Profiling in Information Retrieval. Technology Letters 4(1), 50–56 (2000)Google Scholar
  24. 24.
    Virrantaus, K., Veijalainen, J., Markkula, J., Katasonov, A., Garmash, A., Tirri, H., Terziyan, V.: Developing GIS-Supported Location-Based Services. In: Proc. of WGIS 2001 – First International Workshop on Web Geographical Information Systems, Kyoto, Japan, pp. 423–432 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Vagan Terziyan
    • 1
  • Oleksandra Vitko
    • 2
  1. 1.Department of Mathematical Information TechnologyUniversity of JyvaskylaJyvaskylaFinland
  2. 2.Department of Artificial IntelligenceKharkov National University of RadioelectronicsKharkovUkraine

Personalised recommendations