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University of Malta CSA3080: Lecture 11 © Chris Staff 1 of 20 CSA3080: Adaptive Hypertext Systems I Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 11: User Modelling
University of Malta CSA3080: Lecture 11 © Chris Staff 2 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 3 of 20 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!
University of Malta CSA3080: Lecture 11 © Chris Staff 4 of 20 Aims and Objectives We will fly through some of the fundamentals and cover user modelling in more depth in CSA4080
University of Malta CSA3080: Lecture 11 © Chris Staff 5 of 20 Uses of user models Plan recognition Anticipating behaviour/user actions User interests Information filtering User ability
University of Malta CSA3080: Lecture 11 © Chris Staff 6 of 20 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)
University of Malta CSA3080: Lecture 11 © Chris Staff 7 of 20
University of Malta CSA3080: Lecture 11 © Chris Staff 8 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 9 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 10 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 11 of 20 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: Book reference:
University of Malta CSA3080: Lecture 11 © Chris Staff 12 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 13 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 14 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 15 of 20 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…
University of Malta CSA3080: Lecture 11 © Chris Staff 16 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 17 of 20 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
University of Malta CSA3080: Lecture 11 © Chris Staff 18 of 20 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 (iui.pdf)http://www.aitel.hist.no/~svendah/ahs.html
University of Malta CSA3080: Lecture 11 © Chris Staff 19 of 20 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 Available on-line at:
University of Malta CSA3080: Lecture 11 © Chris Staff 20 of 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
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