“…In fact, this problem has received a considerable amount of attention in another context, the context of recommender systems that seek to predict the rating that a user would give in e-commerce or online streaming websites to an item based on his or her interest (e.g., books, movies, songs). Many studies (e.g., Menon et al, 2011;Forsati et al, 2014;Lika, Kolomvatsos, & Hadjiefthymiades, 2014;Ling, Lyu, & King, 2014;Pereira & Hruschka, 2015;Barjasteh et al, 2016;Fernández-Tobías et al 2016;Contratres et al, 2018) proposed data mining and machine learning techniques (specifically, collaborative filtering algorithms) to address the cold-start problem using the side information about existing users (i.e., users' attributes) to make recommendations for new users with similar profiles. However, most of their approaches focus heavily on the prediction of the new user's rates on a given set of items, lacking the psychometric component i.e., assessment of the users' latent traits.…”