University of Malta CSA4080: Topic 4 © Chris Staff 1 of CSA4080: Adaptive Hypertext Systems II Dr. Christopher Staff Department of Computer Science & AI University of Malta Topic 4: User Modelling

University of Malta CSA4080: Topic 4 © Chris Staff 2 of Aims and Objectives Short history of User Modelling In CSA3080 we covered some of the different approaches to user modelling... –Empirical Quantitative vs. Analytical Cognitive... and Rich’s taxonomies –Canonical vs. Individual –Explicit vs. Implicit –Long-term vs. Short-term

University of Malta CSA4080: Topic 4 © Chris Staff 3 of Aims and Objectives In this lecture, we’ll cover the implementation approaches to user modelling... –Attribute-Value Pairs –Naïve Bayesian... and types of user model... –Overlay, Differential, Perturbation and capturing user behaviour

University of Malta CSA4080: Topic 4 © Chris Staff 4 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 5 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 6 of 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; Allen, Cohen & Perrault)

University of Malta CSA4080: Topic 4 © Chris Staff 7 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 8 of History of User Modelling Although UM has its roots in dialog 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

University of Malta CSA4080: Topic 4 © Chris Staff 9 of History of User Modelling GUMS –Adaptive system developers can define stereotype hierarchies –Prolog facts describe stereotype membership requirements –Rules for reasoning about them

University of Malta CSA4080: Topic 4 © Chris Staff 10 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 11 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 12 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 13 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 14 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 15 of 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.

University of Malta CSA4080: Topic 4 © Chris Staff 16 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 17 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 18 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 19 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 20 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 21 of Overview of AHA! Adaptive Hypertext for All! Each time use 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)

University of Malta CSA4080: Topic 4 © Chris Staff 22 of 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)

University of Malta CSA4080: Topic 4 © Chris Staff 23 of 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)

University of Malta CSA4080: Topic 4 © Chris Staff 24 of Simple Baysian 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

University of Malta CSA4080: Topic 4 © Chris Staff 25 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 26 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 27 of 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 & K learns user’s interests to mark up links, and to construct search engine query webb-umuai-2001.pdf, ProbUserModel.pdf

University of Malta CSA4080: Topic 4 © Chris Staff 28 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 29 of Simple Baysian 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

University of Malta CSA4080: Topic 4 © Chris Staff 30 of 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)...

University of Malta CSA4080: Topic 4 © Chris Staff 31 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 32 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 33 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 34 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 35 of Logic-based We can also partition assumptions about the user

University of Malta CSA4080: Topic 4 © Chris Staff 36 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 37 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 38 of Stereotype-based Kobsa points out that developer of stereotypes needs to fulfill 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

University of Malta CSA4080: Topic 4 © Chris Staff 39 of 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 Polh points out that all user models (that learn about the user) must infer assumptions about the user ( pohl1999-logic- based.pdf )

University of Malta CSA4080: Topic 4 © Chris Staff 40 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 41 of 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 expert’s knowledge Plan means moving from user’s current state to state that user wants to achieve

University of Malta CSA4080: Topic 4 © Chris Staff 42 of 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-)

University of Malta CSA4080: Topic 4 © Chris Staff 43 of 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?

University of Malta CSA4080: Topic 4 © Chris Staff 44 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 45 of 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)

University of Malta CSA4080: Topic 4 © Chris Staff 46 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 47 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 48 of Student modelling See neena_albi_honours.pdf for more examples of student models... We’ll look at ITS in more detail towards the end of the lecture series

University of Malta CSA4080: Topic 4 © Chris Staff 49 of Making Assumptions about the user Browsing behaviour –What does a user’s browsing behaviour tell us about the user?

University of Malta CSA4080: Topic 4 © Chris Staff 50 of Making Assumptions about the user Searle (1969)... when a speech act is performed certain presuppositions must have been valid for the speaker to perform the speech act correctly (from UMUAI-kobsa.pdf, 1995-COOP95- kobsa.pdf)

University of Malta CSA4080: Topic 4 © Chris Staff 51 of Making Assumptions about the user 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 kobsa.pdf

University of Malta CSA4080: Topic 4 © Chris Staff 52 of Making Assumptions about the user 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 kobsa.pdf

University of Malta CSA4080: Topic 4 © Chris Staff 53 of Making Assumptions about the user If the user requests additional details for a hotword, then he is assumed to be familiar with this hotword kobsa.pdf

University of Malta CSA4080: Topic 4 © Chris Staff 54 of 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 –...

University of Malta CSA4080: Topic 4 © Chris Staff 55 of Understanding Browsing Behaviour What might each of these actions mean? Can we relate them to Kobsa’s assumptions? –Do we need link analysis first?

University of Malta CSA4080: Topic 4 © Chris Staff 56 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 57 of Identifying Browsing Behaviour Search browsing General Purpose Browsing The serendipitous user catledge95.pdf

University of Malta CSA4080: Topic 4 © Chris Staff 58 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 59 of 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

University of Malta CSA4080: Topic 4 © Chris Staff 60 of 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