Comparing Relevance Feedback Techniques on German News Articles

Zusammenfassung
We draw a comparison on the behavior of several relevance feedback techniques on a corpus of German news articles. In contrast to the standard application of relevance feedback, no explicit user query is given and the main goal is to recognize a user’s preferences and interests in the examined data collection. The compared techniques are based on vector space models and probabilistic models. The results show that the performance is category-dependent on our data and that overall the vector space approach Ide performs best.
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  • BibTeX
Romberg, J., (2017). Comparing Relevance Feedback Techniques on German News Articles. In: Mitschang, B., Nicklas, D., Leymann, F., Schöning, H., Herschel, M., Teubner, J., Härder, T., Kopp, O. & Wieland, M. (Hrsg.), Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband. Bonn: Gesellschaft für Informatik e.V.. (S. 301-310).
@inproceedings{mci/Romberg2017,
author = {Romberg, Julia},
title = {Comparing Relevance Feedback Techniques on German News Articles},
booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband},
year = {2017},
editor = {Mitschang, Bernhard AND Nicklas, Daniela AND Leymann, Frank AND Schöning, Harald AND Herschel, Melanie AND Teubner, Jens AND Härder, Theo AND Kopp, Oliver AND Wieland, Matthias} ,
pages = { 301-310 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
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ISBN: 978-3-88579-660-2
ISSN: 1617-5468
Datum: 2017
Sprache: en (en)
Typ: Text/Conference Paper
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