University of Malta CSA3080: Lecture 4 © Chris Staff 1 of 14 CSA3080: Adaptive Hypertext Systems I Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 4: Aims, Objectives, and Assumptions of IR, Hypertext, and UM

University of Malta CSA3080: Lecture 4 © Chris Staff 2 of 14 Aims and Objectives Adaptive Hypertext Systems need Hypertext, User Modelling, and Domain Modelling, and a mechanism for comparing the user model and the domain model –General purpose AHSs tend to use IR techniques to represent the domain –ITSs frequently use deeper “semantic” representations, eg, conceptual graphs

University of Malta CSA3080: Lecture 4 © Chris Staff 3 of 14 Aims and Objectives We informally introduce IR and hypertext, to compare their objectives, assumptions, similarities and differences We’ll also talk about UM, and its relationship with IR and hypertext

University of Malta CSA3080: Lecture 4 © Chris Staff 4 of 14 Objectives of IR To represent documents in a collection To facilitate document retrieval from the collection User query represents information need Matching algorithm compares user query to document representations Matching documents presented as “relevant” Results may be ranked in order of relevance

University of Malta CSA3080: Lecture 4 © Chris Staff 5 of 14 Spectrum of Indexing Methods

University of Malta CSA3080: Lecture 4 © Chris Staff 6 of 14 Objectives of Hypertext A (not so new) reading system Represents an information space (typically as a graph) Related information can be “linked” Users navigate through hyperspace by traversing links To enable users to choose which path to follow

University of Malta CSA3080: Lecture 4 © Chris Staff 7 of 14 Assumptions of IR The user can describe the information need The information need can be (sufficiently) described using keywords/terms A document matching the query will be suitable for the particular user (expert v novice) A single document contains the information

University of Malta CSA3080: Lecture 4 © Chris Staff 8 of 14 Assumptions of Hypertext The user can find a relevant document by following links Links will connect related information Related information is linked!

University of Malta CSA3080: Lecture 4 © Chris Staff 9 of 14 IR/Hypertext Similarities Users can seek information –IR: Query matching –Hypertext (HT): Browsing Collections of documents –IR: Similar documents will have similar representations (keywords)? –HT: Similar documents will be linked?

University of Malta CSA3080: Lecture 4 © Chris Staff 10 of 14 IR/Hypertext Differences User interaction: –HT: Follow link - most systems don’t directly support search –IR: Submit query - Most systems don’t directly support linking Relevant info: –IR: relevant info stored in single document –HT: can be spread over multiple, linked documents

University of Malta CSA3080: Lecture 4 © Chris Staff 11 of 14 IR/Hypertext Differences Organisation: –HT: graph (or network), in which related documents are linked (at best) –IR: (at best) clusters of similar documents, (at worst) no organisation.

University of Malta CSA3080: Lecture 4 © Chris Staff 12 of 14 User Modelling Represent interesting “features of the user” [Brusilovsky96] Used in many different domains Reference: –Kobsa, A. (1993). User Modeling: Recent Work, Prospects and Hazards, in M. Schneider-Hufschmidt, T. Kühme and U. Malinowski, eds. (1993): Adaptive User Interfaces: Principles and Practice. North-Holland, Amsterdam, ( kobsa.pdf)

University of Malta CSA3080: Lecture 4 © Chris Staff 13 of 14 User Modelling Many different ways of representing interests, goals, beliefs, preferences However the user is modelled, the information that he/she can be given is only as good as the representation of the domain!

University of Malta CSA3080: Lecture 4 © Chris Staff 14 of 14 Conclusion Information Retrieval, Hypertext, and User Modelling underpin general-purpose Adaptive Hypertext Systems We’ve taken a look at the objectives, assumptions, similarities, and differences between IR and HT