Personality-aware recommendations: an empirical study in education
by Yong Zheng; Archana Subramaniyan
International Journal of Grid and Utility Computing (IJGUC), Vol. 12, No. 5/6, 2021

Abstract: Recommender systems have been developed to deliver item recommendations to the users tailored to user preferences. The impact of the human personality has been realised in user decision makings. There are several personality-aware recommendation models which incorporate the personality traits into the recommendations. They have been demonstrated to be effective in improving the quality of the recommendations in several domains, including movies, music and social networks. However, the impact on the area of education is still under investigation. In this paper, we discuss and summarise state-of-the-art personality-based collaborative filtering techniques for recommendations and perform an empirical study on an educational data. Particularly, we collect the personality traits in two ways - user survey and a natural language processing system. We examine the effectiveness of the recommendation models by using these subjective and inferred personality traits, respectively. Our experimental results reveal that students with different personality traits may make different choices, and the inferred personality traits are more reliable and effective to be used in the process of recommendations.

Online publication date: Fri, 07-Jan-2022

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