Emotional states can play an important role in the process of decision making. Researchers have demonstrated the impact of emotions on the effectiveness of recommender systems. Affective recommender systems (ARS) or emotion-aware recommender systems (EARS) are usually associated with multidisciplinary research, including artificial intelligence, human factors, mood or emotions, facial expressions, and physiological information with human–computer interaction.

The development of affective recommender systems promotes various research topics, such as user interaction and interfaces, algorithm design and evaluations, computational efficiency, deep learning-based recommendation models, and recommendation explanations. This Special Issue on “Affective Recommender Systems” aims to promote new theoretical models, approaches, algorithms, and applications related to ARS. Possible topics include but are not limited to:

Novel and effective models and algorithms for ARS/EARS;
New approaches to utilize emotions in recommender systems;
Review mining or sentimental analysis to assist ARS/EARS;
User-centric studies and evaluations in ARS/EARS;
Recommendation explanations in ARS/EARS;
Novel applications in ARS/EARS;
Emotion detection or recognition in recommender systems;
Emotion representation or representation learning in recommender systems;
Novel paradigms and theoretical foundations in ARS/EARS;
Preference elicitation in ARS/EARS;
User interface design and user-adaptive interaction in ARS/EARS.

Dr. Yong Zheng
Dr. María N. Moreno García
Guest Editors


URL: https://www.mdpi.com/journal/applsci/special_issues/affective_recommender_systems