2011 | OriginalPaper | Buchkapitel
Improving User Stereotypes through Machine Learning Techniques
verfasst von : Teresa M. A. Basile, Floriana Esposito, Stefano Ferilli
Erschienen in: Digital Libraries and Archives
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Users of Digital libraries require more intelligent interaction functionality to satisfy their needs. In this perspective, the most important features are flexibility and capability of adapting these functionalities to specific users. However, the main problem of current systems is their inability to support different needs of individual users due both to their inability to identify those needs, and, more importantly, to insufficient mapping of those needs to the available resources/services. The approaches considered in this paper to tackle such problems concern the use of Machine Learning techniques to adapt the set of user stereotypes with the aim of modelling user interests and behaviour in order to provide the most suitable service. A purposely designed simulation scenario was exploited to show the applicability of the proposal.