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Comparing Relevance Feedback Techniques on German News Articles

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dc.contributor.authorRomberg, Julia
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-21T11:24:40Z
dc.date.available2017-06-21T11:24:40Z
dc.date.issued2017
dc.description.abstractWe 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.en
dc.identifier.isbn978-3-88579-660-2
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-266
dc.subjectRelevance Feedback
dc.subjectText Mining
dc.subjectFiltering Systems
dc.titleComparing Relevance Feedback Techniques on German News Articlesen
dc.typeText/Conference Paper
gi.citation.endPage310
gi.citation.publisherPlaceBonn
gi.citation.startPage301
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleStudierendenprogramm

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P266 - BTW2017 - Datenbanksysteme für Business, Technologie und Web - Workshopband