1. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 1 of 16 cstaff@cs.um.edu.mt CSA3080: Adaptive Hypertext Systems I Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 13: Adaptation Techniques II: Case Studies

2. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 2 of 16 cstaff@cs.um.edu.mt 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

3. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 3 of 16 cstaff@cs.um.edu.mt 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

4. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 4 of 16 cstaff@cs.um.edu.mt 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

5. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 5 of 16 cstaff@cs.um.edu.mt 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

6. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 6 of 16 cstaff@cs.um.edu.mt 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

7. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 7 of 16 cstaff@cs.um.edu.mt 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

8. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 8 of 16 cstaff@cs.um.edu.mt 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

9. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 9 of 16 cstaff@cs.um.edu.mt 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.

10. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 10 of 16 cstaff@cs.um.edu.mt 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

11. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 11 of 16 cstaff@cs.um.edu.mt 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

12. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 12 of 16 cstaff@cs.um.edu.mt 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

13. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 13 of 16 cstaff@cs.um.edu.mt 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. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 14 of 16 cstaff@cs.um.edu.mt 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. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 15 of 16 cstaff@cs.um.edu.mt 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

16. University of Malta CSA3080: Lecture 13 © 2003- Chris Staff 16 of 16 cstaff@cs.um.edu.mt 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