CSA3080: Adaptive Hypertext Systems I

1. CSA3080:Adaptive Hypertext Systems I Lecture 11:User Modelling Dr. Christopher Staff Department of Computer Science & AI University of Malta 1 of 20 cstaff@cs.um.edu.mt

2. Aims and Objectives • Adaptive systems in general need to represent the user in some way so that the system (interface and/or data) can be adapted to reflect the user's interests, needs and requirements • The representation of the user is called a user profile or a user model 2 of 20 cstaff@cs.um.edu.mt

3. Aims and Objectives • UM has its roots in philosophy/AI, and the first implementations were in the field of natural-language dialogue systems • For adaptive systems, user model must learn (at least some of the) user requirements/preferences • User models can be simple or complex, but remember that you can only get out of them what you put in! 3 of 20 cstaff@cs.um.edu.mt

4. Aims and Objectives • We will fly through some of the fundamentals and cover user modelling in more depth in CSA4080 4 of 20 cstaff@cs.um.edu.mt

5. Uses of user models • Plan recognition • Anticipating behaviour/user actions • User interests • Information filtering • User ability 5 of 20 cstaff@cs.um.edu.mt

6. Why a user model is required in AHS • A user model is required to adapt hyperspace to reflect the users preferences, needs and requirements • The level of adaptation in hypertext systems is summarised in the following diagram (we will return to this in Lectures 12/13) 6 of 20 cstaff@cs.um.edu.mt

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8. Types of User Model • Two main types of user model • Analytical Cognitive • Empirical Quantitative • Reference: • G. Brajnik, G. Guida and C. Tasso, "User Modelling in Intelligent Information Retrieval,” in Information Processing and Management, Vol. 23, 1987, pp. 305-320 8 of 20 cstaff@cs.um.edu.mt

9. Empirical Quantitative • Empirical quantitative models make no effort to understand or reason about the user • Contain surface knowledge about the user • Knowledge about the user is taken into consideration explicitly only during the design of the system and is then hardwired into the system (early expert systems) • E.g., models for novice, intermediate, expert users • Fit the current user into one of the stored models 9 of 20 cstaff@cs.um.edu.mt

10. Analytical Cognitive • Try to simulate the cognitive user processes that are taking place during permanent interaction with the system • These models incorporate an explicit representation of the user knowledge • The integration of a knowledge base that stores user modelling information allows for the consideration of specific traits of various users 10 of 20 cstaff@cs.um.edu.mt

11. Taxonomies of User Models • Rich classifies analytical user models along three dimensions • Rich, E.A. (1983): 'Users are Individuals : Individualising User Models', in International Journal of Man-Machine Studies, Volume 18. • Gloor, P. (1997), Elements of Hypermedia Degisn, Part I (Structuring Information) Chapter 2 (user Modeling) Section 1 (Classifications and Taxonomy). • Section reference: http://www.ickn.org/elements/hyper/cyb13.htm • Book reference: http://www.ickn.org/elements/hyper/hyper.htm 11 of 20 cstaff@cs.um.edu.mt

12. 1st Dimension: Canonical vs. Individual • Canonical User Model • User model caters for one single, typical user • Individual User Model • Model tailors its behaviour to many different users 12 of 20 cstaff@cs.um.edu.mt

13. 2nd: Explicit & Implicit • Explicit User Model • User create model himself/herself • E.g., selecting preferences in a Web portal • Implicit User Model • UM built automatically by observing user behaviour • Makes assumptions about the user 13 of 20 cstaff@cs.um.edu.mt

14. 3rd: Long-term vs. short-term • Long-term user models • Capture and manipulate long term user interests • Can be many and varied • Frequently difficult to determine to which interest the current interest belongs • Info changes slowly over time 14 of 20 cstaff@cs.um.edu.mt

15. 3rd: Long-term vs. short-term • Short-term user models • Attempts to build user model within single session • Very small amount of time available • Not necessarily well defined user need • user might not be familiar with terminology • Short-term interest can become long term interest… 15 of 20 cstaff@cs.um.edu.mt

16. Simple User Model Architecture • Attribute-Value Pairs • Attributes are terms/concepts/variables/facts that are significant to the system • Values can be Boolean or Reals/Strings • Example: IR Query (set of terms) • terms indicate user interest • Combinations of attributes can represents concepts 16 of 20 cstaff@cs.um.edu.mt

17. Simple User Model Architecture • User model must be consistent with the domain model • Imagine that you are modelling a user of an information retrieval system • Pointless knowing that the user is an expert physicist if you don’t know how to use it to modify the information space 17 of 20 cstaff@cs.um.edu.mt

18. Adapted from Horgen, S.A., 2002, "A Domain Model for an Adaptive Hypertext System based on HTML", MSc Thesis, Chapter 4 (Adaptivity), pg. 32. Available on-line from http://www.aitel.hist.no/~svendah/ahs.html (iui.pdf) 18 of 20 cstaff@cs.um.edu.mt

19. Adaptive Hypertext Systems “[B]y adaptive hypermedia we mean all hypertext and hypermedia systems which reflect some features of the user in the user model and apply this model to adapt various visible aspects of the system to the user” Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia, in User Modeling and User-Adapted Interaction 6 (2-3), pp. 87-129. Available on-line at: http://www.contrib.andrew.cmu.edu/~plb/UMUAI.ps 19 of 20 cstaff@cs.um.edu.mt

20. Conclusion • User Models can represent user beliefs, preferences, interests, proficiencies, attitudes, goals • User models are used in AHS to modify hyperspace • In IR to select better (more relevant) documents • More likely to use analytical cognitive model, but can still use “simple” models 20 of 20 cstaff@cs.um.edu.mt