A quality based recommender system to disseminate information in a university digital library

Recommender systems evaluate and filter the great amount of information available on the Web, so they could be used in an academic environment to help users in their searches of relevant information. In the literature, a lot of approaches for generating personalized recommendations of information items in such environment can be found. Usually, these approaches use user profiles and/or features of items to predict those relevant items, but they do not take into account the quality of these items. To overcome this problem, in this paper we propose a new recommender system based on quality. This system uses the quality of the items to estimate their relevance. The system measures the item quality and takes into account this measure like a new factor to be considered in the recommendation process. In such a way, we present a recommender system based on items’ quality, to help users to access relevant research resources. This recommender systems is developed by using a fuzzy linguistic approach and it has been tested satisfactorily in a university digital library.