Special Issue "Affective Computing and Recommender Systems"
Deadline for manuscript submissions: 30 June 2023 | Viewed by 2903
Interests: recommender systems; user modeling; web intelligence
Interests: data mining; web mining; machine learning; deep learning; recommender system; decision support in medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue in Information: Selected Papers from the 1st International Electronic Conference on Information
Special Issue in Future Internet: Deep Learning in Recommender Systems
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 Computing and Recommender Systems” aims to promote new theoretical models, approaches, algorithms, and applications related to ARS. Possible topics include but are not limited to:
Topics in Affective Computing
- Emotion recognition and detection;
- Sensing and analysis of human emotions;
- Sentimental analysis;
- Emotion corpora and analysis;
- Affect-based information retrieval;
- Affect-based decision making;
- Affective modeling;
- Affective analysis for human factors (e.g., personality traits, trust, etc.).
Topics in ARS/EARS
- 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
Manuscript Submission Information
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- affective computing
- recommender systems
- affective recommender systems
- emotion-aware recommender systems
- human factors