Applying machine learning techniques to improve user acceptance on ubiquitous environement

TL;DR

This paper explores applying machine learning to enhance user acceptance in ubiquitous environments by adapting information access based on social group data and user feedback, addressing initial interaction challenges.

Contribution

It introduces a method that uses machine learning to adapt ubiquitous system behavior based on social group information and user feedback, improving early user acceptance.

Findings

Improved user acceptance during initial interactions

Effective adaptation based on social group data

Enhanced system-user interaction quality

Abstract

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.