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dc.contributor.authorGolian, Christián
dc.contributor.authorKuchař, Jaroslav
dc.contributor.editorSteinberger, Josef
dc.contributor.editorZíma, Martin
dc.contributor.editorFiala, Dalibor
dc.contributor.editorDostal, Martin
dc.contributor.editorNykl, Michal
dc.identifier.citationSTEINBERGER, Josef ed.; ZÍMA, Martin ed.; FIALA, Dalibor ed.; DOSTAL, Martin ed.; NYKL, Michal ed. Data a znalosti 2017: sborník konference, Plzeň, Hotel Angelo 5. - 6. října 2017. 1. vyd. Plzeň: Západočeská univerzita v Plzni, 2017, s. 51-55. ISBN 978-80-261-0720-0.cs
dc.format5 s.cs
dc.publisherZápadočeská univerzita v Plznics
dc.rights© Západočeská univerzita v Plznics
dc.subjectnovinové doporučenícs
dc.subjectpravidla přidruženícs
dc.subjectCLEF NewsREELcs
dc.titleRecommending news articles using rule-based classifieren
dc.typekonferenční příspěvekcs
dc.description.abstract-translatedIn this paper we summarize our experiments with a rule-based classi-fier as a recommender within CLEF NewsREEL 2017 challenge. Systems that recommend news articles are suitable to solve information overflow in digital editions of newspapers, when users have problems choosing what they want to read. They face challenges unknown to the systems recommending books or movies such as a frequency of producing the new content. This paper deals with an approach based on association rules acting as a classifier. In our approach we experimented with settings that allow reducing the amount of rules used for the classification and increasing the performance that is crucial for real recommen-dations.en
dc.subject.translatednews recommenderen
dc.subject.translatedassociation rulesen
dc.subject.translatedCLEF NewsREELen
Appears in Collections:Data a znalosti 2017
Data a znalosti 2017

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/26335

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