Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries
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AuthorPorcel Gallego, Carlos Gustavo; Ching-López, Alberto; Bernabé Moreno, Juan; Tejada Lorente, Álvaro; Herrera Viedma, Enrique
Korea Information Processing Society (KIPS)
Digital librariesDissemination of informationFuzzy linguistic modelingRecommender system
Carlos Porcel, Alberto Ching-López, Juan Bernabé-Moreno, Alvaro Tejeda-Lorente, & Enrique Herrera-Viedma (2017). Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries. Journal of Information Processing Systems, 13(4), 653-667. [DOI: 10.3745/JIPS.04.0035]
The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, 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 of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user’s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.