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CS3200: Adaptive Hypertext Systems

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Title: CS3200: Adaptive Hypertext Systems


1
CS3200Adaptive Hypertext Systems
Topic 5 User Modelling
  • Dr. Christopher Staff
  • Department of Computer Science AI
  • University of Malta

2
Aims and Objectives
  • Background to user modelling
  • User model implementations
  • Types of user model
  • Undertanding user behaviour

3
Part I Background
4
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

5
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!

6
Uses of user models
  • Plan recognition
  • Anticipating behaviour/user actions
  • User interests
  • Information filtering
  • User ability

7
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

8
(No Transcript)
9
Classifications of User Model
  • Two main classifications 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

10
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

11
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

12
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.
    (http//www.cs.utexas.edu/users/ear/IJMMS.pdf)
  • 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/hy
    per/cyb13.htm
  • Book reference http//www.ickn.org/elements/hyper
    /hyper.htm

13
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

14
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

15
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

16
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

17
History of User Modelling
  • UM and its history are linked to the history of
    user-adaptive systems
  • Based on the way in which the UM updates its
    model of the user, the domain in which it is
    used, and the way the interface is caused to
    change

18
History of User Modelling
  • For instance, UM ratings stereotype/probabilis
    tic recommender system
  • UM hypertext adaptation rules AHS
  • UM user goals pedagogy adaptation rules
    ITS
  • UM representation, and how it learns about its
    users tends to depend on the domain

19
History of User Modelling
  • Focusing on generic user modelling
  • Has its roots in dialogue systems and philosophy
  • Need to model the participants to disambiguate
    referents, model the participants beliefs, etc.
  • Early systems (pre-mid-1985) had user modelling
    functionality embedded within other system
    functionality (e.g., Rich (recommendation
    system) Allen, Cohen Perrault (dialogue
    processing))

20
History of User Modelling
  • From 1985, user modelling functionality was
    performed in a separate module, but not to
    provide user modelling services to arbitrary
    systems
  • So one branch of user modelling focuses on user
    modelling shell systems

2001-UMUAI-kobsa (UM history).pdf
21
History of User Modelling
  • Although UM has its roots in dialogue systems and
    philosophy, more progress has been made in
    non-natural language systems and interfaces
    (PontusJ.pdf)
  • GUMS (General User Modeling System) first to
    separate UM functionality from application - 1986
    (Finin)

22
History of User Modelling
  • GUMS
  • Adaptive system developers can define stereotype
    hierarchies
  • Prolog facts describe stereotype membership
    requirements
  • Rules for reasoning about them

23
History of User Modelling
  • At runtime
  • GUMS collects new facts about users using the
    application system
  • Verifies consistency
  • Informs application of inconsistencies
  • Answers application queries about assumptions
    about the user

24
History of User Modelling
  • Kobsa, 1990, coins User Modeling Shell System
  • UMT (Brajnik Tasso, 1994)
  • Truth maintenance system
  • Uses stereotypes
  • Can retract assumptions made about users

25
History of User Modelling
  • BGP-MS (Kobsa Pohl, 1995)
  • Beliefs, Goals, and Plans - Maintenance System
  • Stereotypes, but stored and managed using
    first-order predicate logic and terminological
    logic
  • Can be used as multi-user, multi-application
    network server

26
History of User Modelling
  • Doppelgänger (Orwant, 1995)
  • Info about user provided via multi-modal user
    interface
  • User model that can be inspected and edited by
    user

27
History of User Modelling
  • TAGUS (Paiva Self, 1995)
  • Also has diagnostic subsystem and library of
    misconceptions
  • Predicts user behaviour and self-diagnoses
    unexpected behaviour
  • um (Kay, 1995)
  • Uses attribute-value pairs to represent user
  • Stores evidence for its assumptions

28
History of User Modelling
  • From 1998 and with the popularisation of the Web,
    web personalisation grew in the areas of targeted
    advertising, product recommendations,
    personalised news, portals, adaptive hypertext
    systems, etc.

29
Part II UM Implementations
30
What might we store in a UM?
  • Personal characteristics
  • General interests and preferences
  • Proficiencies
  • Non-cognitive abilities
  • Current goals and plans
  • Specific beliefs and knowledge
  • Behavioural regularities
  • Psychological states
  • Context of the interaction
  • Interaction history

PontusJ.pdf, ijcai01-tutorial-jameson.pdf
31
From where might we get input?
  • Self-reports on personal characteristics
  • Self-reports on proficiencies and interests
  • Evaluations of specific objects
  • Responses to test items
  • Naturally occurring actions
  • Low-level measures of psychological states
  • Low-level measures of context
  • Vision and gaze tracking

ijcai01-tutorial-jameson.pdf
32
Techniques for constructing UMs
  • Attribute-Value Pairs
  • Machine learning techniques Bayesian
    (probabilistic)
  • Logic-based (e.g.inference techniques or
    algorithms)
  • Stereotype-based
  • Inference rules

kules.pdf
33
Attribute-Value Pairs
  • e.g., ah2002AHA.pdf
  • The representation of the user and of the domain
    are inextricably linked
  • What we want to do is capture the degree to
    which a user knows or is interested in some
    concept
  • We can then use simple or complex rules to update
    the UM and to adapt the interface

34
Attribute-Value Pairs
  • Particularly useful for showing (simple)
    dependencies between concepts
  • Complex ones harder to update
  • Can use IF-THEN-ELSE rules to trigger events
  • Such as updating a user model
  • Modifying the contents of a document (AHA!,
    MetaDoc)
  • Changing the visibility or viability of links

35
Overview of AHA!
  • Adaptive Hypertext for All!
  • Each time user visits a page, a set of rules
    determines how the user model is updated
  • Inclusion rules determine the fragments in the
    current page that will be displayed to the user
    (adaptive presentation)
  • Requirement rules change link colours to indicate
    the desirability of each link (adaptive
    navigation)

36
Attribute-Value Pairs
  • From where do the attributes come?
  • Need to be meaningful in the domain (domain
    modelling)
  • Can be concepts (conceptual modelling)
  • Can be terms that occur in documents (IR)

37
Attribute-Value Pairs
  • What do values represent?
  • Degrees of interest, knowledge, familiarity, ...
  • Skill level, proficiency, competence
  • Facts (usually as strings, rather than numerical
    values)
  • Truth or falsehood (boolean)

38
Simple Bayesian Classifier
  • Rather than pre-determining which concepts, etc.,
    to model, let features be selected based on
    observation
  • SBCs are also used in machine learning approaches
    to user modeling
  • Instead of working with predetermined sets of
    models, learn interests of current user

ProbUserModel.pdf
39
Simple Bayesian Classifier
  • Lets say we want to determine if a document is
    likely to be interesting to a user
  • We need some prior examples of interesting and
    non-interesting documents
  • Automatically select document features
  • Usually terms of high frequency
  • Assign boolean values to terms in vectors
  • To indicate presence in or absence from document

40
Simple Bayesian Classifier
  • Now, for an arbitrary document, we want to
    determine the probability that the document is
    interesting to the user
  • P(classj word1 word2 ... wordk)
  • Assuming term independence, the probability that
    an example belongs to classj is proportional to

41
Syskill Webert
  • Learns simple Baysian classifier from user
    interaction
  • User identifies his/her topic of interest
  • As user browses, rates web pages as hot or
    cold
  • S W learns users interests to mark up links,
    and to construct search engine query

webb-umuai-2001.pdf, ProbUserModel.pdf
42
Syskill Webert
  • Text is converted to feature vectors (term
    vectors) for SBC
  • Terms used are those identified as being most
    informative words in current set of pages
  • based on the expected ability to classify
    document if the word is absent from doc

43
Simple Bayesian Classifier
  • Of course, the term independence assumption is
    unrealistic, but SBC still works well
  • Algorithm is fast, so can be used to update user
    model in real time
  • Can be modified to support ranking according to
    degree of probability, rather than boolean

44
Simple Bayesian Classifier
  • Needs to be trained, usually using small data
    sets
  • Works by multiplying probability estimates to
    obtain joint probabilities
  • If any is zero, results will be zero...
  • Can use small constant e (0.001) instead
    (estimation bias) ...

45
Personal WebWatcher
  • Predicting interesting hyperlinks from the set of
    documents visited by a user
  • Followed links are positive examples of user
    interests
  • Ignored links are negative examples of user
    interests
  • Use descriptions of hyperlinks as shortened
    documents rather than full docs

pwwTR.pdf
46
Personal WebWatcher
  • Also uses a simple bayesian classifier to
    recommend interesting links
  • where
  • TF(w, c) is term frequency of term w in document
    of class c (e.g., interesting/non-interesting),
    and TF(w, doc) is frequency of term w in document
    doc

47
Personal WebWatcher
  • Training set is set of documents that user has
    seen and user could have seen but has ignored
  • Uses short description of document, rather than
    document vector itself

48
Logic-based
  • Does a UM only contain facts about a users
    knowledge?
  • Can we also represent assumptions, and
    assumptions about beliefs?
  • Assumptions are contextualised, and represented
    using modal logic (ATac, or assumption
    typeassumption content)

pohl1999-logic-based.pdf
49
Logic-based
  • We can also partition assumptions about the user

50
Logic-based
  • Advantage is that beliefs, assumptions, facts are
    already in logical representation
  • Makes it easier to draw conclusions about the
    user from the stored knowledge

51
Stereotype-based
  • Originally proposed by Rich in 1979
  • Captures default information about groups of
    users
  • Tends not to be used anymore

1993-aui-kobsa.pdf
52
Stereotype-based
  • Kobsa points out that developer of stereotypes
    needs to fulfil three tasks
  • Identify user subgroups
  • Identify key characteristics of typical user in
    subgroup
  • So that new user may be automatically classified
  • Represent hierarchically ordered stereotypes
  • Fine-grained vs. coarse-grained

53
Inference rules
  • e.g., C-Tutor, avanti.pdf
  • May use production rules to make inferences about
    user
  • Also, to update system about changes in user
    state or user knowledge
  • Note that Pohl points out that all user models
    (that learn about the user) must infer
    assumptions about the user (pohl1999-logic-based.p
    df)

54
Adaptive Hypertext Systems
  • By 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.cm
    u.edu/plb/UMUAI.ps

55
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/s
vendah/ahs.html (iui.pdf)
56
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

57
Part III Types of UM
58
Types of User Models
  • User Models have their roots in philosophy and
    learning
  • Student models assumed to be some subset of the
    knowledge about the domain to be learnt
  • Consequently, the types of user model have been
    heavily influenced by this

59
Student Models
  • Student Models are used, e.g., in Intelligent
    Tutoring Systems (ITSs)
  • In ITS we know user goals, and may be able to
    identify user plans
  • The domain/experts knowledge must be well
    understood
  • Assumption that user wants to acquire experts
    knowledge
  • Plan means moving from users current state to
    state that user wants to achieve

60
Student Models
  • If we assume that experts knowledge is
    transferable to student, then students knowledge
    includes some of the experts knowledge
  • Overlay, differential, perturbation models (from
    neena_albi_honours.pdf p25-)

61
Overlay Models
  • SCHOLAR (Carbonell, 1970)
  • Simplest of the student models
  • Student knowledge (K) is a subset of experts
  • Assumes that K missing from student model is not
    known by the student
  • But what if student has incorrectly learnt K?

62
Overlay Models
  • Good when subject matter can be represented as
    prerequisite hierarchy
  • K remaining to be acquired by student is exactly
    difference between expert K and student K
  • Cannot represent/infer student misconceptions

63
Differential Models
  • WEST (Burton Brown, 1989)
  • Compares student/expert performance in execution
    of current task
  • Divides K into K the student should know (because
    it has already been presented) and K the student
    cannot be expected to know (yet)

64
Differential Models
  • Still assumes that students K is subset of
    experts
  • But can differentiate between K that has been
    presented but not understood and K that has not
    yet been presented

65
Perturbation Models
  • LMS (Sleeman Smith, 1981)
  • Combines overlay model with representation of
    faulty knowledge
  • Bug library
  • Attempts to understand why student failed to
    complete task correctly
  • Permits student model to contain K not present in
    experts K

66
Part IV Understanding user behaviour
67
Making assumptions about users
  • Browsing behaviour
  • What does a users browsing behaviour tell us
    about the user?

68
Making assumptions about users
  • Searle (1969)... when a speech act is performed
    certain presuppositions must have been valid for
    the speaker to perform the speech act correctly
    (from 1995-UMUAI-kobsa.pdf, 1995-COOP95-kobsa.pdf)

69
Making assumptions about users
  • If the user requests an explanation, a graphic,
    an example or a glossary definition for a
    hotword, then he is assumed to be unfamiliar with
    this hotword.

1996-kobsa.pdf
70
Making assumptions about users
  • If the user unselects an explanation, a graphic,
    an example or a glossary definition for a
    hotword, then he is assumed to be familiar with
    this hotword.

1996-kobsa.pdf
71
Making assumptions about users
  • If the user requests additional details for a
    hotword, then he is assumed to be familiar with
    this hotword.

1996-kobsa.pdf
72
User Actions in Hypertext
  • Actions that can be performed in hypertext
  • Follow link
  • Dont follow link
  • Print
  • Bookmark
  • Go to bookmark
  • Backup
  • Go to URL
  • ...

73
Understanding Browsing Behaviour
  • What might each of these actions mean?
  • Can we relate them to Kobsas assumptions?
  • Do we need link analysis first?

74
Identifying Browsing Behaviour
  • Lost in Hyperspace (otter2000.pdf)
  • Honing in on information
  • Needing more help/information
  • Being un/familiar with topic/web space
  • Interested in topic
  • Uninterested in topic
  • Changing topic

75
Identifying Browsing Behaviour
  • Search browsing
  • General Purpose Browsing
  • The serendipitous user

catledge95.pdf
76
Understanding Browsing Behaviour
  • How can understanding browsing behaviour help us
    create better adaptive hypertext systems?
  • Less intrusive
  • Just-in-Time support
  • Dont give more info when it is not
    required/wanted
  • Efficient use of resources

77
Conclusions
  • The ability to model the user allows reasoning
    about the user to tailor an interaction to the
    users needs and requirements...
  • ... especially when the user is unable to
    describe what it is they need
  • Tightly bound to domain/expert knowledge

78
Conclusions
  • Significant efforts to decouple the user model
    from the application
  • May be too expensive to accurately model all
    domains, and in any case, goal of many adaptive
    systems is not to help user become expert, but to
    provide timely assistance at the right level of
    detail
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