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CSA3212: User Adaptive Systems

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.

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CSA3212: User Adaptive Systems

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

  2. Aims and Objectives • Adaptive navigation in Letizia, Personal WebWatcher, WebWatcher, and HyperContext • Adaptive Presentation in MetaDoc

  3. 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

  4. Adaptive Navigation • Adaptive Navigation-local reconnaissance is highly related to link annotation • E.g., Letizia, WebWatcher, Personal WebWatcher, HyperContext

  5. 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?”)

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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...

  19. 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.

  20. 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

  21. 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

  22. 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

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