An improved recommender system to avoid the persistent information overload in a university digital library

• Autorzy: Porcel C. , Morales del Castillo J. M. , Cobo M. J. , Ruiz A. A. , Herrera-Viedma E.
• Języki publikacji: EN

Abstrakty

EN

Nowadays we are continuously bombarded with a lot of information, and because of it we have serious problems with accessing the relevant information, that is, we suffer from the information overload problems. Recommender systems have been applied successfully to avoid the information overload in different domains, but the number of electronic resources daily generated keeps growing and the problem rises again. Therefore, we find a persistent problem of information overload. In this paper we propose an improved recommender system to avoid the persistent information overload found in a University Digital Library. The idea is to include a memory to remember selected resources but not recommended to the user, and in such a way, the system could incorporate them in future recommendations to complete the set of filtered resources, for example, if there are a few resources to be recommended or if the user wishes output obtained by combination of resources selected in different recommendation rounds.

Słowa kluczowe

EN

recommender systems; information overload; university digital libraries; fuzzy linguistic modeling

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2010
• Tom: Vol. 39, no 4
• Strony: 899--923
• Opis fizyczny: Bibliogr. 49 poz., wykr.

Twórcy

autor

Porcel C.

autor

Morales del Castillo J. M.

autor

Cobo M. J.

autor

Ruiz A. A.

autor

Herrera-Viedma E.

• University of Jaen Department of Computer Science, Jaen, Spain,

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