Abstract
Most advertisement systems widely used in Internet try to improve advertisement process by targeting specific groups of potential customers. Many systems exploit the information directly provided by the user and the data collected by monitoring user activities in order to built accurate user profiles, which determines the success of the advertisement process.
This paper presents a solution to the problem of targeting advertisement information when minimal knowledge about anonymous internet user is given. In particulary as, for example, in the case of search engines, the user remains anonymous and his interaction with the service can be very limited. In this case the information about him is sparse and based only on the keywords and the data submitted by the HTTP request. The proposed architecture is based on the use of predefined profiles and the computation of fuzzy similarities in order to match the observed user with appropriate target profiles. The notion of fuzzy similarity presented here is based on the theoretical framework of the Łukasiewicz structure, which guarantees the correctness of the approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baudisch, P., Leopold, D.: User-configurable advertising profiles applied to Web page banners. In: Proceedings of the First Berlin Internet Economics Workshop, Berlin (1997)
Burke, R.: Semantic ratings and heuristic similarity for collaborative filtering. In: AAAI Workshop on Knowledge-Based Electronic Markets, AAAI, Menlo Park (2000)
Langheinrich, M., Nakamura, A., Abe, N., Kamba, T., Koseki, Y.: Unintrusive Customization Techniques for Web Advertising. Computer Networks 31(11-16), 1259–1272 (1999)
Luukka, P., Saastamoinen, K., Könönen, V., Turunen, E.: A classifier based on maximal fuzzy similarity in generalised Łukasiewicz structure. In: FUZZ-IEEE 2001, Melbourne, Australia (2001)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., Wiltshire, J.: Discovery of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 9 (2002)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective Personalization Based on Association Rule Discovery from Web Usage Data. In: WIDM 2001, Atlanta (2001)
Morici, C., Niewiadomski, R.: A framework for a personalized advertising on Web based on maximal fuzzy similarity in Łukasiewicz structure. In: Proceedings of IPMU 2004 (2004)
Spiliopoulou, M., Mobasher, B., Berent, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web Usage Analysis. INFORMS Journal on Computings 15
Turunen, E.: Mathematics behind Fuzzy Logic. In: Advances in Soft Computing, Physica-Verlag, Heidelberg (1999)
Web resources for Open Directory Project, http://dmoz.org/about.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Milani, A., Morici, C., Niewiadomski, R. (2004). Fuzzy Matching of User Profiles for a Banner Engine. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24767-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-24767-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22057-2
Online ISBN: 978-3-540-24767-8
eBook Packages: Springer Book Archive