The hypercontext framework for adaptive Hypertext

ABSTRACT

We present HyperContext, a framework for adaptive and adaptable hypertext. Our fundamental premise is that when people encounter the same document, each may interpret the information it contains differently. Usually, the interpretations are not available to future users of the same information. HyperContext permits users to make these interpretations explicit, and provides support to structure hyperspace around interpretations of documents, rather than around the documents themselves. When a user browses through hyperspace, a document's context is used to determine which interpretation to present to the user. We also derive a user model of the user's short-term interests, by first representing the user's interest in the current document as a salient interpretation before combining it with the salient interpretations of other documents accessed by the user on the same path of traversal. This paper describes the adaptive features of the HyperContext framework, and presents the results of an initial evaluation of one of the features.

REFERENCES

• 1: R. Armstrong, D. Freitag, T. Joachims, and T. Mitchell. Webwatcher: learning apprentice for the world wide web. In AAAI Spring Symposium on Information Gathering, pages 6--12, 1995
• 2: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 3: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 4: T. Berners-Lee, J. Hendler, and O. Lassila. The semantic. Scientific American, 284(5):34--43, May 2001
• 5: Paul De Bra , Geert-Jan Houben , Hongjing Wu, AHAM: a Dexter-based reference model for adaptive hypermedia, Proceedings of the tenth ACM Conference on Hypertext and hypermedia : returning to our diverse roots: returning to our diverse roots, p.147-156, February 21-25, 1999, Darmstadt, Germany [doi>10.1145/294469.294508]
• 6: P.Brezillon and S. Abu-Hakima. Using knowledge in its context: Report on the ijcai-93 workshop. AI Magazine, 16(1):87--91, Spring 1995
• 7: P.Brusilovsky. Methods and techniques of adaptive hypermedia. In Adaptive Hypertext and Hypermedia, pages 1--43. Klewer Academic Publishers, Netherlands, 1998
• 8: Chris Buckley , Gerard Salton, Optimization of relevance feedback weights, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.351-357, July 09-13, 1995, Seattle, Washington, United States [doi>10.1145/215206.215383]
• 9: L. A. Carr , W. Hall , S. Hitchcock, Link services or link agents?, Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems, p.113-122, June 20-24, 1998, Pittsburgh, Pennsylvania, United States [doi>10.1145/276627.276640]
• 10: Samhaa R. El-Beltagy , Wendy Hall , David De Roure , Leslie Carr, Linking in context, Proceedings of the twelfth ACM conference on Hypertext and Hypermedia, August 14-18, 2001, Århus, none, Denmark [doi>10.1145/504216.504257]
• 11: Peter W. Foltz , Susan T. Dumais, Personalized information delivery: an analysis of information filtering methods, Communications of the ACM, v.35 n.12, p.51-60, Dec. 1992 [doi>10.1145/138859.138866]
• 12: F. Giunchiglia and P. Bouquet. Introduction to contextual reasoning. An Artificial Intelligence perspective. In B. Kokinov, editor, Perspectives on Cognitive Science, volume 3. NBU Press, Sofia, 1997. Lecture Notes of a course on "Contextual Reasoning" of the European Summer School on Cognitive Science, Sofia, 1996
• 13: Frank Halasz , Mayer Schwartz, The Dexter hypertext reference model, Communications of the ACM, v.37 n.2, p.30-39, Feb. 1994 [doi>10.1145/175235.175237]
• 14: W. Lam , S. Mukhopadhyay , J. Mostafa , M. Palakal, Detection of shifts in user interests for personalized information filtering, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.317-325, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243279]
• 15: J. McCarthy. Notes on formalizing context. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 81--98, Los Altos, CA, 1993. Morgan Kaufmann
• 16: J. McCarthy. A logical ai approach to context. Technical note, Computer Science Department, Stanford University, Stanford, California, USA, 1996
• 17: J. J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The SMART retrieval system: experiments in automatic document processing, pages 313--323. Prentice-Hall, Englewood Cliffs, US, 1971
• 18: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 19: Matthias Schneider-Hufschmidt , Uwe Malinowski , Thomas Kuhme, Adaptive User Interfaces: Principles and Practice, Elsevier Science Inc., New York, NY, 1993
• 20: C. Staff. Hypercontext: Using context in adaptive hypertext. In Proceedings of the International and Interdisciplinary Conference on Modeling and Using Context '97, pages 243--255., 1997
• 21: C. Staff. HyperContext: A Framework for Adaptive and Adaptable Hypertext. PhD thesis, University of Sussex, 2001
• 22: Tak Woon Yan , Matthew Jacobsen , Hector Garcia-Molina , Umeshwar Dayal, From user access patterns to dynamic hypertext linking, Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems, p.1007-1014, May 1996, Paris, France

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.4 Hypertext/Hypermedia

General Terms:
Theory

Keywords:
adaptive hypertext, context, user modelling