1. CSA3212: Topic 5 © 2005- Chris Staff 1 of 78 chris.staff@um.edu.mt University of Malta CSA3212: User-Adaptive Systems Dr. Christopher Staff Department of Intelligent Computer Systems University of Malta Topic 5: User Modelling

2. 2 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Aims and Objectives Background to user modelling User model implementations Types of user model Understanding user behaviour

3. CSA3212: Topic 5 © 2005- Chris Staff 3 of 78 chris.staff@um.edu.mt University of Malta Part I: Background

4. 4 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Aims and Objectives User-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. 5 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 6 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Uses of user models Plan recognition Anticipating behaviour/user actions User interests Information filtering User ability …

7. 7 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Why a user model is required in UAS A user model is required to adapt the information space to reflect the users preferences, needs and requirements, amongst others The level of adaptation in adaptive hypermedia systems is summarised in the following diagram, but can also apply to UASes

8. 8 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta

9. 9 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 10 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 11 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 12 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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/hyper/cyb13.htm Book reference: http://www.ickn.org/elements/hyper/hyper.htm

13. 13 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 14 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 2nd: Explicit & Implicit Explicit User Model User creates 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. 15 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 16 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 17 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 18 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta History of User Modelling For instance, UM + ratings = stereotype/probabilistic 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. 19 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 20 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 21 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 22 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta History of User Modelling GUMS Adaptive system developers can define stereotype hierarchies Prolog facts describe stereotype membership requirements Rules for reasoning about them

23. 23 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 24 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 25 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 26 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 27 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 28 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. CSA3212: Topic 5 © 2005- Chris Staff 29 of 78 chris.staff@um.edu.mt University of Malta Part II: UM Implementations

30. 30 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 31 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 32 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 33 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 34 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 35 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 36 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 37 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 38 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 39 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Simple Bayesian Classifier Let’s 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. 40 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Simple Bayesian Classifier Now, for an arbitrary document, we want to determine the probability that the document is interesting to the user P(class j | word 1 & word 2 &... word k ) Assuming term independence, the probability that an example belongs to class j is proportional to

41. 41 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Syskill & Webert Learns simple Bayesian classifier from user interaction User identifies his/her topic of interest As user browses, rates web pages as “hot” or “cold” S & W learns user’s interests to mark up links, and to construct search engine query webb-umuai-2001.pdf, ProbUserModel.pdf

42. 42 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 43 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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 binary

44. 44 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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  (0.001) instead (estimation bias)...

45. 45 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 46 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 47 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 48 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Logic-based Does a UM only contain facts about a user’s knowledge? Can we also represent assumptions, and assumptions about beliefs? Assumptions are contextualised, and represented using modal logic (AT:ac, or assumption type:assumption content) pohl1999-logic-based.pdf

49. 49 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Logic-based We can also partition assumptions about the user

50. 50 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 51 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 52 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 53 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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.pdf )

54. 54 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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

55. 55 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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) http://www.aitel.hist.no/~svendah/ahs.html

56. 56 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. CSA3212: Topic 5 © 2005- Chris Staff 57 of 78 chris.staff@um.edu.mt University of Malta Part III: Types of UM

58. 58 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Types of User Models User Models have their roots in philosophy and learning Student model 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. 59 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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/expert’s knowledge must be well understood Assumption that user wants to acquire expert’s knowledge Plan means moving from user’s current state to state that user wants to achieve

60. 60 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Student Models If we assume that expert’s knowledge is transferable to student, then student’s knowledge includes some of the expert’s knowledge Overlay, differential, perturbation models (from neena_albi_honours.pdf p25-)

61. 61 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Overlay Models SCHOLAR (Carbonell, 1970) Simplest of the student models Student knowledge (K) is a subset of expert’s Assumes that K missing from student model is not known by the student But what if student has incorrectly learnt K?

62. 62 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 63 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 64 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Differential Models Still assumes that student’s K is subset of expert’s But can differentiate between K that has been presented but not understood and K that has not yet been presented

65. 65 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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 expert’s K

66. CSA3212: Topic 5 © 2005- Chris Staff 66 of 78 chris.staff@um.edu.mt University of Malta Part IV: Understanding user behaviour

67. 67 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Making assumptions about users Browsing behaviour What does a user’s browsing behaviour tell us about the user?

68. 68 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 69 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 70 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 71 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 72 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta User Actions in Hypertext Actions that can be performed in hypertext Follow link Don’t follow link Print Bookmark Go to bookmark Backup Go to URL...

73. 73 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Understanding Browsing Behaviour What might each of these actions mean? Can we relate them to Kobsa’s assumptions? Do we need link analysis first?

74. 74 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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. 75 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Identifying Browsing Behaviour Search browsing “directed search; where the goal is known” General Purpose Browsing “consulting sources that have a high likelihood of items of interest” The serendipitous user “purely random” catledge95.pdf

76. 76 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Understanding Browsing Behaviour How can understanding browsing behaviour help us create better adaptive hypertext systems? Less intrusive Just-in-Time support Don’t give more info when it is not required/wanted Efficient use of resources

77. 77 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta Conclusions The ability to model the user allows reasoning about the user to tailor an interaction to the user’s needs and requirements...... especially when the user is unable to describe what it is they need Tightly bound to domain/expert knowledge

78. 78 of 78 cstaff@cs.um.edu.mt University of Malta CSA3212: Topic 5 © 2005- Chris Staff University of Malta 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