Text learning for user profiling in e-commerce

• Published in: International journal of systems science Vol. 37; no. 13; pp. 905 - 918
• Main Authors: Degemmis, M., Lops, P., Ferilli, S., Di Mauro, N., Basile, T. M. A., Semeraro, G.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 20-10-2006
• Subjects: Content-based filtering; Machine learning; Text categorization; User modeling; Word Net
• ISSN: 0020-7721; 1464-5319
• DOI: 10.1080/00207720600891794

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 article presents a new method, based on the classic Rocchio algorithm for text categorization, able to discover user preferences from the analysis of textual descriptions of items in online catalog of e-commerce Web sites. Experiments have been carried out on several data sets, and results have been compared with those obtained using an inductive logic programming (ILP) approach and a probabilistic one.