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.
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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
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DOI: https://doi.org/10.1007/11527503_45
Publisher Name: Springer, Berlin, Heidelberg
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