Personalized information retrieval based on context and ontological knowledge

  • Authors:

  • Ph. Mylonas;D. Vallet;P. Castells;M. FernÁ/ndez;Y. Avrithis

  • Affiliations:

  • National technical university of athens, image, video and multimedia laboratory, 9, iroon polytechniou street, 15773 zographou, athens, greece/ e-mail: fmylonas@image.ntua.gr, iavr@image.ntua.gr;Universidad autó/noma de madrid, escuela polité/cnica superior, 28049 madrid, spain/ e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es;Universidad autó/noma de madrid, escuela polité/cnica superior, 28049 madrid, spain/ e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es;Universidad autó/noma de madrid, escuela polité/cnica superior, 28049 madrid, spain/ e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es;National technical university of athens, image, video and multimedia laboratory, 9, iroon polytechniou street, 15773 zographou, athens, greece/ e-mail: fmylonas@image.ntua.gr, iavr@image.ntua.gr

  • Venue:
  • The Knowledge Engineering Review
  • Year:
  • 2008

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Abstract

Context modeling has long been acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are (1) the explicit distinction between historic user context and live user context, (2) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and (3) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.