![]() These personality profiles are used to precompute personality-based neighborhoods, which are then used to predict movie ratings and generate recommendations. To explore the performance of such a method, MovieOcean, a movie recommender system that uses a questionnaire based on the Big Five model to generate personality profiles, was implemented. ![]() ![]() This research effort explores the incorporation of personality treats into user-user collaborative filtering algorithms. ![]() Our findings could inform personality-based RS by improving the process of indirect user personality acquisition. 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. 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. In this work, we investigate the possibility of automatically detecting personality from users’ eye movements when interacting with a recommendation interface. However, accurate acquisition of a user’s personality is still a challenging issue. In recommender systems (RS), it has been found that user personality is related to their preferences and behavior, which attracted an increasing attention to the ways to leverage personality into the recommendation process. Personality, according to psychology definition, accounts for individual differences in our enduring emotional, interpersonal, experiential, attitudinal, and motivational styles. Recent research in behavioral decision making demonstrates the advantages of using eye-tracking to surface insights into users’ underlying cognitive processes.
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