Performance Study of Collaborative Filtering Algorithms for Educational Recommender Systems applying Prior Grouping of Users with Similar Personality Traits

• Janderson Jason Barbosa Aguiar Federal University of Campina Grande (UFCG)
• Joseana Macêdo Fechine Federal University of Campina Grande (UFCG)
• Evandro de Barros Costa Federal University of Alagoas (UFAL)

DOI: https://doi.org/10.5753/cbie.sbie.2020.1082

Abstract

Some research on Educational Recommender Systems uses the Trait Theory (Personality Traits — PT) when applying Collaborative Filtering (CF). In this paper, we start from the idea that all CF algorithms implicitly deal with users’ PT, and by explicitly grouping users based on these PT, the algorithms’ performance can improve. In this research, we conducted an experimental study on CF algorithms’ accuracy, applying a previous grouping of users (mostly computing students) based on their PT (Big Five model). With the applied strategy, the results indicated that there is the possibility of improving the accuracy of neighborhood-based CF algorithms in the educational domain.

Keywords: Educational Recommender Systems, Collaborative Filtering, Personality Traits

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