User Modelling is the core component for the majority of personalisation systems. By keeping a model for every user, a system can successfully personalise its content and utilise available resources accordingly. While researching the literature, one can recognize the importance of achieving interoperability across various platforms and systems while attempting to personalise a large diversity of web resources. Furthermore, scrutable solutions allow users to control any modelling process that uses their information. Finally, privacy of user data while exchanging user models from one source to another must be taken in mind. With this paper, a Scrutable User Modelling Infrastructure is presented which blends together these user modelling 1ingredients' and, by adopting Semantic Web technologies, attempts to model a range of life-long user interactions with a variety of web-based systems from the educational, business and social networking domains.
[1]
Peter Dolog,et al.
Challenges and Benefits of the Semantic Web for User Modelling
,
2003
.
[2]
Eric Horvitz,et al.
Principles of Lifelong Learning for Predictive User Modeling
,
2007,
User Modeling.
[3]
James A. Hendler,et al.
The Semantic Web" in Scientific American
,
2001
.
[4]
Alfred Kobsa,et al.
Privacy-enhanced personalization
,
2006,
FLAIRS.
[5]
Lora Aroyo,et al.
Interoperability in Personalized Adaptive Learning
,
2006,
J. Educ. Technol. Soc..
[6]
Judy Kay,et al.
Intelligent Tutoring Systems
,
2000,
Lecture Notes in Computer Science.
[7]
Peter Dolog,et al.
A Framework for Browsing, Manipulating and Maintaining Interoperable Learner Profiles
,
2005,
User Modeling.