Grant: Research Tools and Instruments
University of British Columbia
$123,076.00
Eye-tracking for Intelligent Personalization of Information Visualization
FY 2018-19
Summary
User-adaptive interaction, a field at the intersection of artificial intelligence (AI) and human-computer interaction (HCI), aims to create intelligent interactive systems that provide users with a personalized interaction experience by modelling and adapting in real-time to relevant users' needs and abilities. The benefits of user-adaptive interaction have been shown for a variety of tasks and applications (Jameson 2009). This proposal supports research at the forefront of this field by investigating a new domain of application for user-adaptive interaction: user-adaptive visualizations*Infovis is becoming increasingly important given the continuous growth of applications that allow users to view and manipulate complex data, not only in professional settings, but also for personal usage (e.g., monitoring fitness, tracking interactions in social media, understanding home resources consumption (Huang et al. 2015). To date, visualizations are typically designed based on the type of tasks and data to be handled, without taking into account user differences. However, there is mounting evidence that visualization effectiveness depends on a user's specific preferences, abilities, states, and even personality. The eye-tracking equipment requested in this proposal will be instrumental to investigate how to create visualizations that can infer the relevant user's characteristics from how users look at them, and personalized the visualization accordingly, to best suit the users needs and abilities. *
Program
Research Tools and Instruments
Recipient
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RTI-2019-00711
Name nom
Conati, Cristina
Department département
Computer Science
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2019
Award amount
123076
Program id
RTI
Program name en
Research Tools and Instruments
Program name fr
Outils et instruments de recherche
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1607
Committee name en
Computer, Mathematical and Statistical Sciences (RTI)
Committee name fr
Sciences informatiques, mathématiques et statistiques (OIR)
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Partie
1
Nb partie
1
Application title
Eye-tracking for Intelligent Personalization of Information Visualization
Keyword
NSERC
Application summary
User-adaptive interaction, a field at the intersection of artificial intelligence (AI) and human-computer interaction (HCI), aims to create intelligent interactive systems that provide users with a personalized interaction experience by modelling and adapting in real-time to relevant users' needs and abilities. The benefits of user-adaptive interaction have been shown for a variety of tasks and applications (Jameson 2009). This proposal supports research at the forefront of this field by investigating a new domain of application for user-adaptive interaction: user-adaptive visualizations*Infovis is becoming increasingly important given the continuous growth of applications that allow users to view and manipulate complex data, not only in professional settings, but also for personal usage (e.g., monitoring fitness, tracking interactions in social media, understanding home resources consumption (Huang et al. 2015). To date, visualizations are typically designed based on the type of tasks and data to be handled, without taking into account user differences. However, there is mounting evidence that visualization effectiveness depends on a user's specific preferences, abilities, states, and even personality. The eye-tracking equipment requested in this proposal will be instrumental to investigate how to create visualizations that can infer the relevant user's characteristics from how users look at them, and personalized the visualization accordingly, to best suit the users needs and abilities. *
