Abordagem híbrida de recomendação de conteúdo baseado em tags adaptativas aplicada em bibliotecas digitais

Abstract

The technological evolution of the library in the academic environment brought a lot of information and documents that are available to access, but not always these systems have mechanisms to search in an integrated way the relevant information for the user. To alleviate this problem, we propose a recommendation system that generates the user profile through tags that are reshaped over time. Tags are terms or words that convey meaning and are generally used by users to classify certain content. To trace the user profile the system uses information from your lending history stored in the library database and it collects their opinions (feedback) through a list of recommendations. These data are integrated with the document base of institutional repository. Thus, the recommendation system assists users in identifying relevant items and makes suggestions for content in an integrated environment that contains institutional repository documents and the university library database. The proposed recommendation system uses a hybrid approach being applied in an academic environment with the participation of users.