The World Wide Web is a huge information source for Digital Libraries, but it has a significant problem: the information overload. Therefore, the main challenge is to support users to locate the right information at the right time. In this paper we present the Profile Extractor, a personalization component, based on Machine Learning techniques, which allows for the discovery of preferences, needs and interests of users accessing to COVAX (Contemporary Culture Virtual Archives in XML) digital library. Moreover, we examined how user profiles can be exploited in the Covax digital library to improve the retrieval process.

Learning Preferences of Users Accessing Digital Libraries

LOPS, PASQUALE;SEMERARO, Giovanni;
2003

Abstract

The World Wide Web is a huge information source for Digital Libraries, but it has a significant problem: the information overload. Therefore, the main challenge is to support users to locate the right information at the right time. In this paper we present the Profile Extractor, a personalization component, based on Machine Learning techniques, which allows for the discovery of preferences, needs and interests of users accessing to COVAX (Contemporary Culture Virtual Archives in XML) digital library. Moreover, we examined how user profiles can be exploited in the Covax digital library to improve the retrieval process.
978-905809524-4
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/136498
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