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Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

  • Pablo Castells
  • Miriam Fernández
  • David Vallet
  • Phivos Mylonas
  • Yannis Avrithis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3762)

Abstract

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied.

Keywords

User Preference Retrieval Model User Request Semantic Annotation User Interest 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pablo Castells
    • 1
  • Miriam Fernández
    • 1
  • David Vallet
    • 1
  • Phivos Mylonas
    • 2
  • Yannis Avrithis
    • 2
  1. 1.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain
  2. 2.School of Electrical and Computer EngineeringNational Technical University of AthensZographou, AthensGreece

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