A Relevance Feedback Method for Discovering User Profiles from Text
نویسنده
چکیده
The huge amounts of data on the Internet often make difficult the user’s search for relevant information. For this reason, systems that are able to support users in this task could be a valuable help in this activity. Unfortunately, being able to catch user interests and represent them in a structured form is in general a problematic activity. Our research deals with the application of supervised Machine Learning methods for user profiling in the e-commerce area. The 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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