Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

The significant advances in information and communication technologies are changing the process of howinformation is accessed. The internet is a very important source of information and it influences thedevelopment of other media. Furthermore, the growth of digital content is a big problem for academic digitallibraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes ofdisseminating information in these university digital libraries and that promote access to information ofinterest. These proposals manage to adapt a user’s access to information according to his or her needs andpreferences. As seen in the literature one of the techniques with the best results, is the application ofrecommender systems. These are tools whose objective is to evaluate and filter the vast amount of digitalinformation that is accessible online in order to help users in their processes of accessing information. Inparticular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommendersystems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of thesystem in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguisticrecommender systems to help researchers, students, and teachers access resources of interest and thus,improve and complement the services provided by academic digital libraries. KCI Citation Count: 1