Psychological Models for Personalized Human-Computer Interaction (HCI)

The behavior of users in the digital world, such as online shopping or social media activity, is increasingly supported by personalized systems like recommender systems and personalized learning. Early work on personalized systems was mainly data-driven, based on behavioral data, such as ratings, likes, and purchases. Although these systems are useful for both users and service providers, the main downside is the limited interpretability and explainability of the data. Such limitations in both interpretability and explainability translate in using data without understanding the root-cause of behaviors. Recent work has thus started to adopt a more theory-driven approach by including psychological theories and models to improve personalized systems. These systems take advantage of psychological theories/models to explain and predict behaviors of users, and allow for a deeper understanding of users’ behavior, preferences, and needs, which in turn also lead to more generalizable results.

Moreover, digital behavior has also been used to infer user traits and characteristics. For example, social media activities have been used to predict personality traits and intelligence, whereas the field of affective computing has been active in devising methodologies for inferring emotional states from digital signals.

This Research Topic aims at collecting state-of-the-art research that supports personalized services with psychological theories/models. We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles within the usage of psychological theories/models in personalized Human-Computer Interaction (HCI) on topics including, but not limited to:

- Psychological theories/models that explain online behavior, such as:
• Personality;
• Emotions;
• Cognitive biases and illusions;
• Learning styles;
• Emotional contagion (e.g., in group settings).

- Psychological theories/models to personalize digital interactions, such as in:
• User interfaces;
• Recommendations;
• Social robots and chatbots;
• E-learning.

- Prediction of psychological models drawing data from digital behavior information resources, such as:
• Social media;
• E-commerce;
• Physical activities;
• Online learning;
• Group scenarios (e.g., group recommender systems).