1 / 16

CSA3080: Adaptive Hypertext Systems I

CSA3080: Adaptive Hypertext Systems I. Lecture 13: Adaptation Techniques II: Case Studies. Dr. Christopher Staff Department of Computer Science & AI University of Malta. Aims and Objectives. We have seen the goals and objectives of Adaptive Hypertext Systems

calais
Download Presentation

CSA3080: Adaptive Hypertext Systems I

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CSA3080:Adaptive Hypertext Systems I Lecture 13:Adaptation Techniques II: Case Studies Dr. Christopher Staff Department of Computer Science & AI University of Malta 1 of 16 cstaff@cs.um.edu.mt

  2. Aims and Objectives • We have seen the goals and objectives of Adaptive Hypertext Systems • We have seen how to represent user interests through User Modeling • We have seen how Information Retrieval can be used to search for relevant documents based on a user query 2 of 16 cstaff@cs.um.edu.mt

  3. Aims and Objectives • We will be looking at three different approaches to adaptive Hypertext • Adaptive navigation using link recommendation • Personal WebWatcher • Adaptive presentation using stretch text • MetaDoc • Context-based adaptive navigation • HyperContext 3 of 16 cstaff@cs.um.edu.mt

  4. 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 4 of 16 cstaff@cs.um.edu.mt

  5. 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 5 of 16 cstaff@cs.um.edu.mt

  6. Personal WebWatcher • Architecture • scan the image from the original paper… • 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 6 of 16 cstaff@cs.um.edu.mt

  7. 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 7 of 16 cstaff@cs.um.edu.mt

  8. PWW • 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 8 of 16 cstaff@cs.um.edu.mt

  9. 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. 9 of 16 cstaff@cs.um.edu.mt

  10. 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 10 of 16 cstaff@cs.um.edu.mt

  11. 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 11 of 16 cstaff@cs.um.edu.mt

  12. 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 12 of 16 cstaff@cs.um.edu.mt

  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 13 of 16 cstaff@cs.um.edu.mt

  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 14 of 16 cstaff@cs.um.edu.mt

  15. HyperContext • Context is used in two ways • To create interpretations of documents in context • Interpretation = relevant terms from parent added to child, plus 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 15 of 16 cstaff@cs.um.edu.mt

  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 16 of 16 cstaff@cs.um.edu.mt

More Related