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Comparing Relevance Feedback Techniques on German News Articles

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2017

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Gesellschaft für Informatik e.V.

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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Romberg, Julia (2017): Comparing Relevance Feedback Techniques on German News Articles. Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-660-2. pp. 301-310. Studierendenprogramm. Stuttgart. 6.-10. März 2017

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