Abstract
This paper describes an adaptive electronic video store application that monitors customers’ actions and provides dynamic movie recommendation. The adaptive recommendation is formed based on double stereotypes that have been constructed for user modeling. The construction of stereotypes has been based on a novel approach that uses an Immune Network System (INS). In particular, the INS has been applied on data collected from 150 users of an earlier version of the e-commerce application. Specifically, the INS clustered users’ interests as well as movies and represented each resulting cluster with corresponding antibodies. The double classification (users’ interests – movies) was performed in a hierarchical way that resulted in several levels of user stereotypes: These stereotypes are then used dynamically by the e-commerce application to infer users’ interests in movies based on a small set of observed users’ actions.
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.
References
Ardissono, L., Torasso, P.: Dynamic User Modeling in a Web Store Shell. In: Proceedings of the 14th Conference ECAI, Berlin, Germany, pp. 621–625 (2000)
Brusilovsky, P.: Adaptive Hypermedia. User Modeling and User-Adapted Interaction 11, 87–110 (2001)
Cayzer, S., Aickelin, U.: A Recommender System based on the Immune Network. In: Proceedings of the Congress on Evolutionary Computation (2002)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach, 1st edn. Springer, Heidelberg (2002)
Kay, J.: Stereotypes, student models and scrutability. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 19–30. Springer, Heidelberg (2000)
Morrison, T., Aickelin, U.: An Artifcial Immune System as a Recommender for Web Sites. In: Proceedings of the 1st Conference on ARtifcial Immune Systems (ICARIS-2002), Canterbury, UK, pp. 161–169 (2002)
Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, D.C.: Web Usage Mining as a Tool for Personalization: A Survey. Journal of User Modeling and User-Adapted Interaction 13, 311–372 (2003)
Rich, E.: User Modeling via Stereotypes. Journal of Cognitive Science 3, 329–354 (1979)
Schwarzkopf, E.: An adaptive web site for the UM2001 conference. In: Proceedings of the UM2001 Workshop on Machine Learning for User Modeling, pp. 77–86 (2001)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, San Diego (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Virvou, M., Savvopoulos, A., Tsihrintzis, G.A., Sotiropoulos, D.N. (2007). Constructing Stereotypes for an Adaptive e-Shop Using AIN-Based Clustering. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_94
Download citation
DOI: https://doi.org/10.1007/978-3-540-71618-1_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
Online ISBN: 978-3-540-71618-1
eBook Packages: Computer ScienceComputer Science (R0)