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Exploiting Session Context for Information Retrieval - A Comparative Study

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45157

Pandey,  Gaurav
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44955

Luxenburger,  Julia
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Pandey, G., & Luxenburger, J. (2008). Exploiting Session Context for Information Retrieval - A Comparative Study. In C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, & R. W. White (Eds.), Advances in Information Retrieval: 30th European Conference on IR Research, ECIR 2008 (pp. 652-657). Berlin: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1B9D-6
Zusammenfassung
Hard queries are known to benefit from relevance feedback provided by users. It is, however, also known that users are generally reluctant to provide feedback when searching for information. A natural way to retrieve the most relevant information satisfying the user need without actually demanding any active user participation is to exploit implicit feedback from the previous user search behavior, i.e., from the context of the current search session. In this work, we present a comparative study on the performance of the three most prominent retrieval models, the \emph{vector-space}, \emph{probabilistic}, and \emph{language-model based} retrieval frameworks, when additional session context is incorporated.