Adaptation in automated user-interface design
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
Statistical profiles of highly-rated web sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine Learning for Adaptive User Interfaces
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
An Adaptive User Interface Based On Personalized Learning
IEEE Intelligent Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Restyling website design via touch-based interactions
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond
ACM Transactions on Interactive Intelligent Systems (TiiS)
Designing effective behaviors for educational embodied agents
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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With the growing need for intelligent software, exploring the potential of Machine Learning (ML) algorithms for User Interface (UI) adaptation becomes an ultimate requirement. The work reported in this paper aims at enhancing the UI interaction by using a Rule Management Engine (RME) in order to handle a training phase for personalization. This phase is intended to teach to the system novel adaptation strategies based on the end-user feedback concerning his interaction (history, preferences...). The goal is also to ensure an adaptation learning by capitalizing on the user feedbacks via a promoting/demoting technique, and then to employ it later in different levels of the UI development.