CDL (Corporate Digital Library) is a prototypical intelligent digital library service that is currently being developed at the University of Bari, as an evolution of a previous project named IDL (Intelligent Digital Library). Among the characterizing features of CDL there are a retrieval engine and several facilities available for the library users. In this paper, we present the web-based visual environment we have developed with the aim of improving user-library interaction. The CDL environment is equipped with some novel visual tools that are primarily intended for inexperienced users, who represent most of the users that usually have access to digital libraries. Machine Learning techniques have been exploited in CDL for document analysis, classification, and understanding, as well as for building a user modeling module, which is the basic component for providing CDL with user interface adaptivity. This feature is also discussed in the paper.

An Adaptive visual environment for digital libaries

COSTABILE, Maria;ESPOSITO, Floriana;SEMERARO, Giovanni;FANIZZI, Nicola
1999-01-01

Abstract

CDL (Corporate Digital Library) is a prototypical intelligent digital library service that is currently being developed at the University of Bari, as an evolution of a previous project named IDL (Intelligent Digital Library). Among the characterizing features of CDL there are a retrieval engine and several facilities available for the library users. In this paper, we present the web-based visual environment we have developed with the aim of improving user-library interaction. The CDL environment is equipped with some novel visual tools that are primarily intended for inexperienced users, who represent most of the users that usually have access to digital libraries. Machine Learning techniques have been exploited in CDL for document analysis, classification, and understanding, as well as for building a user modeling module, which is the basic component for providing CDL with user interface adaptivity. This feature is also discussed in the paper.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/129208
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