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Personalized information retrieval based on context and ontological knowledge

Published online by Cambridge University Press:  01 March 2008

PH. MYLONAS
Affiliation:
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
D. VALLET
Affiliation:
Universidad Autónoma de Madrid, Escuela Politécnica Superior, 28049 Madrid, Spain; e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es
P. CASTELLS
Affiliation:
Universidad Autónoma de Madrid, Escuela Politécnica Superior, 28049 Madrid, Spain; e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es
M. FERNÁNDEZ
Affiliation:
Universidad Autónoma de Madrid, Escuela Politécnica Superior, 28049 Madrid, Spain; e-mail: david.vallet@uam.es, pablo.castells@uam.es, miriam.fernandez@uam.es
Y. AVRITHIS
Affiliation:
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

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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2008

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