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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Learning User Profiles from Text in e-Commerce
verfasst von : M. Degemmis, P. Lops, S. Ferilli, N. Di Mauro, T. M. A. Basile, G. Semeraro
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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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.