CSA3212: User Adaptive Systems Lecture 8: Case Studies Dr. Christopher Staff Department of Computer Science & AI University of Malta

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” Persistence of interest: just because you’ve stopped searching for something, doesn’t mean you’re no longer interested in it... 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 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 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 a learner 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... Used mainly in ITS

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: Coined by Ted Nelson, 1971 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: Concept Level: 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