An Adaptive Hypermedia System Using a Constraint Satisfaction Approach for Information Personalization

  • Syed Sibte Raza Abidi
  • Yan Zeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)


Adaptive hypermedia systems offer the functionality to personalize the information experience as per a user-model. In this paper we present a novel content adaptation approach that views information personalization as a constraint satisfaction problem. Information personalization is achieved by satisfying two constraints: (1) relevancy constraints to determine the relevance of a document to a user and (2) co-existence constraints to suggest complementing documents that either provide reinforcing viewpoints or contrasting viewpoints, as per the user’s request. Our information personalization framework involves: (a) an automatic constraint acquisition method, based on association rule mining on a corpus of documents; and (b) a hybrid of constraint satisfaction and optimization methods to derive an optimal solution—i.e. personalized information. We apply this framework to filter news items using the Reuters-21578 dataset.


Constraint Satisfaction Constraint Satisfaction Problem Unary Constraint Association Rule Mining Content Adaptation 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Syed Sibte Raza Abidi
    • 1
  • Yan Zeng
    • 1
  1. 1.NICHE Research Group, Faculty of Computer ScienceDalhousie University HalifaxCanada

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