Learning User Profiles from Text in e-Commerce

• Published in: Advanced Data Mining and Applications pp. 370 - 381
• Main Authors: Degemmis, M., Lops, P., Ferilli, S., Di Mauro, N., Basile, T. M. A., Semeraro, G.
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Inductive Logic Programming; Memory organisation. Data processing; Recommender System; Relevance Feedback; Software; Text Categorization; Textual Description; Data analysis; Probabilistic approach; Electronic document; Text; Trade catalog; Data mining; Digital information; Relevance; Linguistics; Electronic trade; Inductive logic programming; Web site; User behavior
• ISBN: 354027894X; 9783540278948
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11527503_45

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.