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Affective video recommender systems: A survey.

Dandan Wang1, Xiaoming Zhao1

  • 1Department of Computer Science, Taizhou University, Taizhou, China.

Frontiers in Neuroscience
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Affective video recommender systems (AVRS) leverage viewer emotions via physiological and physical signals to overcome traditional system limitations. This survey analyzes 92 articles on AVRS, covering algorithms, databases, and evaluation methods for future research.

Keywords:
affective analysisaffective video recommender systemdeep learningmultidisciplinemultimodalneurosciencephysiological signalsvideo recommendation

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Area of Science:

  • Multidisciplinary Human-Robot Interaction (HRI)
  • Affective Computing
  • Neuroscience
  • Computer Science

Background:

  • Traditional video recommenders struggle with insufficient data, leading to the cold-start problem.
  • Affective Video Recommender Systems (AVRS) integrate diverse data sources for enhanced personalization.

Purpose of the Study:

  • To provide a comprehensive overview of the Affective Video Recommender System (AVRS) domain.
  • To analyze recent advancements in affective video recommendation by reviewing 92 published articles.

Main Methods:

  • Collected and analyzed 92 relevant articles from Google Scholar.
  • Categorized and summarized findings concerning AVRS algorithms (traditional and deep learning-based).
  • Reviewed common AVRS databases, audience response categories, and evaluation methodologies.

Main Results:

  • AVRS utilizes physical signals (facial expressions, speech) and physiological signals (ECG, EDA, EEG, GSR, PPG) for emotion analysis.
  • Physiological signals offer reliable, involuntary indicators of emotional states.
  • The survey covers various recommendation algorithms, datasets, and evaluation metrics within the AVRS field.

Conclusions:

  • Affective analysis technologies in AVRS effectively address the cold-start problem.
  • Identified challenges and potential future research directions in affective video recommendation.