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. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3584 LNAI, pp. 370–381). Springer Verlag. https://doi.org/10.1007/11527503_45
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