Using memory to reduce the information overload in a university digital library

In the recent times the amount of information coming overwhelms us, and because of it we have serious problems to access to relevant information, that is, we suffer information overload problems. Recommender systems have been applied successfully to avoid the information overload in different scopes, but the number of electronic resources daily generated keeps growing and the problem still remain. 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.