Abstract
In this paper, we propose a framework named UMT (User-profile Modeling based on Transactional data) for modeling user group profiles based on the transactional data. UMT is a generic framework for application systems that keep the historical transactions of their users. In UMT, user group profiles consist of three types: basic information attributes, synthetic attributes and probability distribution attributes. User profiles are constructed by clustering user transaction data and integrating cluster attributes with domain information extracted from application systems and other external data sources. The characteristic of UMT makes it suitable for personalization of transaction-based commercial application systems. A case study is presented to illustrate how to use UMT to create a personalized tourism system capable of using domain information in intelligent ways and of reacting to external events.
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References
Adomavicius, G., Tuzhilin, A.: Using Data Mining Methods to Build Customer Profiles. IEEE Computer 34(2), 74–82 (2001)
Aedissono, L., Goy, A.: Tailoring the Interaction with Users in Web Stores. In: User Modeling and User-Adapted Interaction, vol. 10(4), pp. 251–303. Kluwer Academic Publishers, Dordrecht (2000)
Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: INTRIGUE: personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence, Special Issue on Artificial Intelligence for Cultural Heritage and Digital Libraries 17, 687–714 (2003)
Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. Inf. Syst. 25(5), 345–366 (2000)
Cavique, L.: Micro-segmentação de Clientes com Base em Dados de Consumo: Modelo RM-Similis. Revista Portuguesa e Brasileira de Gestão 2(3), 72–77
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256–265. Morgan Kaufmann, San Francisco (1998)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Kobsa, A., Pohl, W.: The BP-MS User Modeling System. User Modeling and User-Adapted Interaction 4(2), 59–106 (1995)
MacDonald, R., Silver, D.: Web-based User Profiling Using a Recurrent Neural Network. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 472–477. ACTA Press (2002)
Marques, N., Gonçalves, S.: Applying a Part-of-Speech Tagger to Postal Address Detection on the Web. In: Proceedings of the IV International Conference on Language Resources and Evaluation, LREC 2004, Lisboa, Portugal, vol. I, pp. 287–290 (2004)
Pazzani, M., Muramatsu, J., Billsus, D.: Syskill and Webert: Identifying Interesting Web Sites. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 54–61. AAAI Press, Menlo Park (1996)
Rucker, J., Polanco, M.: Siteseer: Personalized Navigation for the Web. Communication of ACM 40(3), 73–75 (1997)
Yang, Y., Guan, X., You, J.: CLOPE: a fast and effective clustering algorithm for transactional data. In: KDD 2002, pp. 682–687. ACM Press, New York (2002)
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Yang, Y., Marques, N.C. (2005). User Group Profile Modeling Based on User Transactional Data for Personalized Systems. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_34
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DOI: https://doi.org/10.1007/11595014_34
Publisher Name: Springer, Berlin, Heidelberg
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