Toward Changing Users behavior with Emotion-based Adaptive Systems

Mina Alipour*, Mahyar T. Moghaddam, Karthik Vaidhyanathan, Mikkel Baun Kjærgaard

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities.Emotions are integral parts of human nature, and ignoring that can lead the interactive systems to failure, low quality, or discomfort.User interfaces (UIs) are increasingly becoming adaptive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions.However, the previous approaches proposed for supervising such adaptations are not effectively adopted in real-life problems.This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Reinforcement Learning (MFRL).The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction.We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger.We performed experiments with a mobile application we developed that acts as a recommender system in emergency training.By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions.The research includes literature analysis, surveys, and further adopting an iterative process in implementation and experimentation.The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.

Original languageEnglish
Title of host publicationUMAP 2023 : Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery
Publication dateJun 2023
ISBN (Electronic)9781450399326
Publication statusPublished - Jun 2023
Event31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023 - Limassol, Cyprus
Duration: 26. Jun 202330. Jun 2023


Conference31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023
SponsorACM SIGCHI, ACM SIGWEB, NSF, Springer Nature, User Modeling Inc.


  • Emergency
  • Emotions
  • Reinforcement Learning
  • Self-adaptation
  • User Interface


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