Applying machine learning techniques to improve user acceptance on ubiquitous environement
This addresses user acceptance issues in ubiquitous environments, but it appears incremental as it applies existing machine learning techniques to a specific adaptation scenario.
The paper tackles the problem of adapting information access in ubiquitous systems to users when only their social group is known, not their personal interests, by using machine learning to associate actions with perceived user situations based on feedback. The result is a method that improves user acceptance of the system at the beginning of interaction.
Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to adapt the information access provided by ubiquitous systems to users when the system only knows the user social group, without knowing anything about the user interest. The adaptation procedures associate actions to perceived situations of the user. Associations are based on feedback given by the user as a reaction to the behavior of the system. Our method brings a solution to some of the problems concerning the acceptance of the system by users when applying machine learning techniques to systems at the beginning of the interaction between the system and the user.