Recent research in behavioral decision making demonstrates the advantages of using eye-tracking to surface insights into users’ underlying cognitive processes. Personality, according to psychology definition, accounts for individual differences in our enduring emotional, interpersonal, experiential, attitudinal, and motivational styles. In recommender systems (RS), it has been found that user ... [Show full abstract] personality is related to their preferences and behavior, which attracted an increasing attention to the ways to leverage personality into the recommendation process. However, accurate acquisition of a user’s personality is still a challenging issue. In this work, we investigate the possibility of automatically detecting personality from users’ eye movements when interacting with a recommendation interface. Specifically, we report an experiment that harnesses two recommendation interfaces to collect eye-movement data in several product domains and then utilize the data to predict the users’ Big-Five personality traits through various machine learning methods. The results show that AdaBoost combined with Gini index score-based feature selector predicts the traits most accurately, and interface- and domain-specific data allow to improve the accuracy of personality trait predictions. Our findings could inform personality-based RS by improving the process of indirect user personality acquisition.