Addressing the Challenges of Survey Fatigue for Lifelong User Modelling: Initialising Baseline Models Using Community Psychometric Values

2012 ◽  
Author(s):  
Victoria Macarthur ◽  
Owen Conlan
Keyword(s):  
User Modelling ◽  
Psychometric Values

User modelling for intent prediction in information analysis

2003 ◽  
Author(s):  
Eugene Santos ◽  
Hien Nguyen ◽  
Qunhua Zhao ◽  
Hua Wang
Keyword(s):  
User Modelling ◽  
Information Analysis ◽  
Intent Prediction

scholarly journals SUM'20: State-based User Modelling

2020 ◽  
Author(s):  
Sahan Bulathwela ◽  
María Pérez-Ortiz ◽  
Rishabh Mehrotra ◽  
Davor Orlic ◽  
Colin de la Higuera ◽  
...  
Keyword(s):  
User Modelling

The Amsterdam Alexithymia Scale: Its Psychometric Values and Correlations with Other Personality Traits

1999 ◽  
Vol 68 (5) ◽  
pp. 241-251 ◽  
Author(s):  
Bob Bermond ◽  
Harrie C.M. Vorst ◽  
Ad J.J.M. Vingerhoets ◽  
Welmoet Gerritsen
Keyword(s):  
Personality Traits ◽  
Psychometric Values

Inclusive User Modelling and Simulation

2012 ◽  
pp. 281-304
Author(s):  
Pradipta Biswas ◽  
Pat Langdon
Keyword(s):  
User Modelling ◽  
Modelling And Simulation

Can user models be learned at all? Inherent problems in machine learning for user modelling

2004 ◽  
Vol 19 (1) ◽  
pp. 61-88 ◽  
Author(s):  
MARTIN E. MÜLLER
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
User Model ◽  
User Modelling ◽  
Machine Learning Algorithms ◽  
Learning Problem ◽  
User Models ◽  
Modelling Techniques ◽  
Modelling Problem

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

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