Abstract
In the Internet era, huge amounts of data are available to everybody, in every place and at any moment. Searching for relevant information can be overwhelming, thus contributing to the user’s sense of information overload. Building systems for assisting users in this task is often complicated by the difficulty in articulating user interests in a structured form – a profile – to be used for searching. Machine learning methods offer a promising approach to solve this problem. Our research focuses on supervised methods for learning user profiles which are predictively accurate and comprehensible.
The main goal of this paper is the comparison of two different approaches for inducing user profiles, respectively based on Inductive Logic Programming (ILP) and probabilistic methods. An experimental session has been carried out to compare the effectiveness of these methods in terms of classification accuracy, learning and classification time, when coping with the task of learning profiles from textual book descriptions rated by real users according to their tastes.
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
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Degemmis, M., Lops, P., Semeraro, G., Abbattista, F.: Extraction of user profiles by discovering preferences through machine learning. In: Klopotek, M.A., Wierzhon, S.T., Trojanowski, K. (eds.) Information Systems: New Trends in Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 69–78. Springer, Heidelberg (2003)
Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy Theory Revision: Induction and abduction in INTHELEX. Machine Learning 38(1/2), 133–156 (2000)
Lewis, D.D.: Naive (Bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Lewis, D.D., Ringuette, M.: A comparison of two learning algorithms for text categorization. In: Proceedings of SDAIR 1994, 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, US, pp. 81–93 (1994)
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)
Moulinier, I., Ganascia, J.G.: Confronting an existing machine learning algorithm to the text categorization task. In: IJCAI, Workshop on New approaches to Learning for Natural Language Processing, Montréal (1995)
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)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In: Proceedings of the Fifth International Conference on Computer and Information Technology, East West University, Bangladesh (2002)
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)
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
Esposito, F. et al. (2004). Evaluation and Validation of Two Approaches to User Profiling. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds) Web Mining: From Web to Semantic Web. EWMF 2003. Lecture Notes in Computer Science(), vol 3209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30123-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-30123-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23258-2
Online ISBN: 978-3-540-30123-3
eBook Packages: Springer Book Archive