csa3212: user adaptive systems

CSA3212:User Adaptive Systems

Dr. Christopher StaffDepartment of Computer Science & AI

University of Malta

Lecture 8: Case Studies

2 of [email protected] University of Malta

CSA3200: Lecture 8© 2005- Chris Staff

Aims and Objectives Adaptive navigation in Letizia, Personal

WebWatcher, WebWatcher, and HyperContext

Adaptive Presentation in MetaDoc



Aims and Objectives We will look at three different approaches

to adaptive Hypertext Adaptive navigation using link

recommendation Personal WebWatcher

Adaptive presentation using stretchtext MetaDoc

Context-based adaptive navigation HyperContext



Adaptive Navigation Adaptive Navigation-local reconnaissance

is highly related to link annotation E.g., Letizia, WebWatcher, Personal

WebWatcher, HyperContext



Adaptive Navigation Differences in ITS and generic approaches to

adaptive navigation ITS aim is to transfer knowledge efficiently by guiding

through a learning space Learned, ready to be learned, not ready to be learned

Generic aim is to guide user through document space to relevant information (that is ideally also at the level of simplicity required by user!)

Relevant, not relevant (what about “related to long-term interest X?”)



Adaptive Navigation Letizia

Predicts a user’s interest as the user browses and suggests links to relevant document in the vicinity of the user’s current location

User tends to traverse Web graph “downwards”, but relevant information may lie sideways

Observes user behaviour to determine user interests (eg, “skipping” links, bookmarking...)

Makes recommendations based on “persistence of interest” lieberman95letizia.pdf



Adaptive Navigation WebWatcher

Guides users through a web site based on interaction with past users

Users express a query and are guided to relevant documents

Associates what users are interested in with documents that they mark as relevant

Marks up links with terms used by users, and terms that occur in “downstream” documents

webwatcher.ijcai97.pdf



Personal WebWatcher recommends documents to a user based on an analysis of the documents that the user has browsed

References: Mladenic, D. (1996), Personal WebWatcher: design and implementation. Available on-line at

http://www.cs.cmu.edu/afs/cs/project/theo-4/text-learning/www/pww/papers/PWW/pwwTR.ps.Z

Mladenic, D. (1999), Machine learning used by Personal WebWatcher. Available on-line at http://www.cs.cmu.edu/afs/cs/project/theo-4/text-learning/www/pww/papers/PWW/pwwACAI99.ps.gz

Additional information about Personal WebWatcher can be found at http://www.cs.cmu.edu/afs/cs/project/theo-4/text-learning/www/pww/index.html

Personal WebWatcher



Personal WebWatcher PWW observes users of the WWW and

suggests pages that they may be interested in

PWW learns the individual interests of its users from the Web pages that the users visit

The learned user model is then used to suggest new HTML pages to the user



Personal WebWatcher Architecture

a Web proxy server

The proxy saves URLs of visited documents to disk

a learner The learner uses

them to generate a model of user interests

When a user visits a Web page, PWW’s proxy server also analyses out-links

Recommends those similar to user model



Learning the user model Operates in batch mode Revisits all documents visited by user and

those lying one link away Visited documents are +ive examples of

user interests Non-visited are -ive examples



Personal WebWatcher Model used to predict if a page is likely to

be relevant (+ive) or not (-ive) Predictor looks one step ahead from

document requested by user Links in requested document are marked up



HyperContext HyperContext assumes that the scope of

relevance within a document is dependent on its context

Remember that information is data in context…

… knowledge is information used in the correct context



HyperContext HyperContext also assumes that a link is

evidence that the destination document is relevant to the parent (in some way)

Is all of a document relevant in its entirety to all of its parents?

HyperContext says not. Can semi-automatically determine which

regions in the child are relevant to the parent



HyperContext Context is used in two ways

To create interpretations of documents in context

Interpretation = relevant terms from parent added to child, and remove non-relevant terms from child

To construct a short-term model of user interests as a user browses through hyperspace

Pick up relevant terms from the interpretations that are visited and “add” them to user model



HyperContext Interpretations, as well as original

documents, are indexed Query can be automatically extracted from

user model and submitted to IR system User can be guided to relevant information

(link recommendation), or shown “See Also” references



HyperContext Uses Information Retrieval-in-Context to

guide users to information in hyperspace (up to 7 link traversals away)

Once user has navigated to a location which probably contains information, can submit query to search “context sphere”

With Adaptive Information Discovery, system generates query on behalf of user

HCTCh5.pdf



Adaptive Presentation Approaches are generally intended to make

the content more understandable to the user automatically including glossary explanations

of terms unknown to the user removing extraneous information, or

information known to the user showing information in format preferred by

user...



MetaDoc Adaptive presentation of text Documentation reading system that has

hypertext capabilities Reference:

Boyle, C., and Encarnacion, A.O., 1994, “Metadoc: An Adaptive Hypertext Reading System”, in Brusilovsky, et. al. (eds), Adaptive Hypertext and Hypermedia, 71-89, 1998, Netherlands:Kluwer Academic Publishers.



MetaDoc Goal:

“A hypertext document that automatically adapts to the ability level of the reader”

No need for reader to “skip” text, or to look elsewhere for further information



MetaDoc Mechanism:

Stretchtext Coined by Ted Nelson, 1971 Transitions from one level to the next need

to be smooth (HCI) User model used to determine ability level

of user



MetaDoc User Model:

Stereotypes: Novice, beginner, intermediate, expert

Concept Level: Concept levels are associated with stereotypes If user level is lower than the level required to

understand the concept, the text is stretched to explain it

Conversely, more detail is provided to the expert reader

csa3212: user adaptive systems

Documents

chris staffaims

user interests

user browses

relevant information

user adaptive systemsdr

relevant documentsassociates

users current locationuser

adaptive navigationits