Abstract
Exploring digital collections to find information relevant to a user’s interests is a challenging task. Algorithms designed to solve this relevant information problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. This paper presents a new method, based on the classical Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalogues of e-commerce Web sites. Experiments have been carried out on a dataset of real users, and results have been compared with those obtained using an Inductive Logic Programming (ILP) approach and a probabilistic one.
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
Asnicar, F., Tasso, C.: ifweb: a prototype of user model-based intelligent agent for documentation filtering and navigation in the word wide web. In: Proceedings of 1st Int. Workshop on adaptive systems and user modeling on the World Wide Web, pp. 3–12 (1997)
Billsus, D., Pazzani, M.J.: A hybrid user model for news story classification. In: Proceedings of the Seventh International Conference on User Modeling, Banff, Canada, pp. 99–108 (1999)
Esposito, F., Semeraro, G., Ferilli, S., Degemmis, M., Di Mauro, N., Basile, T.M.A., Lops, P.: Evaluation and validation of two approaches to user profiling. In: Proc. of the ECML/PKDD-2003 First European Web Mining Forum, pp. 51–63 (2003)
Lieberman, H.: Letizia: an agent that assists web browsing. In: Nédellec, C., Rouveirol, C. (eds.) Proceedings of the International Joint Conference on Artificial Intelligence, pp. 924–929 (1995)
Magnini, B., Strapparava, C.: Improving user modelling with content-based techniques. In: Proc. of 8th International Conference on User Modeling, pp. 74–83. Springer, Heidelberg (2001)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Mladenic, D.: Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems 14(4), 44–54 (1999)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, San Antonio, US, pp. 195–204. ACM Press, New York (2000)
Orkin, M., Drogin, R.: Vital Statistics. McGraw-Hill, New York (1990)
Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27(3), 313–331 (1997)
Rocchio, J.: Relevance feedback information retrieval. In: Salton, G. (ed.) The SMART retrieval system - experiments in automated document processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., Ferilli, S.: A logic framework for the incremental inductive synthesis of datalog theories. In: Fuchs, N.E. (ed.) LOPSTR 1997. LNCS, vol. 1463, pp. 300–321. Springer, Heidelberg (1998)
Stefani, A., Strapparava, C.: Personalizing access to web sites: The siteif project. In: Proc. of 2nd Workshop on Adaptive Hypertext and Hypermedia (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Degemmis, M., Lops, P., Ferilli, S., Di Mauro, N., Basile, T.M.A., Semeraro, G. (2005). Learning User Profiles from Text in e-Commerce. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_45
Download citation
DOI: https://doi.org/10.1007/11527503_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
eBook Packages: Computer ScienceComputer Science (R0)