News Recommender System based on Association Rules @ CLEF NewsREEL 2017

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F17%3A00312922" target="_blank" >RIV/68407700:21240/17:00312922 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-1866/paper_173.pdf" target="_blank" >http://ceur-ws.org/Vol-1866/paper_173.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

News Recommender System based on Association Rules @ CLEF NewsREEL 2017

Popis výsledku v původním jazyce

Digital editions of newspapers cause information overflow and users have problems choosing what they want to read. Systems which recommend news articles are suitable to solve such problems. Nevertheless, they face challenges unknown to the systems recommending books or movies such as a frequency of producing the new content. CLEF NewsREEL challenge enables to compare and evaluate news recommendation systems in an online and offline task focused on recommending articles to real users and tuning of algorithms respectively. This paper deals with an approach based on association rules acting as a classifier. In our approach we experimented with settings that allows to reduce the amount of rules used for the classification and increase the performance that is crucial for real recommendations. We evaluated our approach in both tasks of the CLEF NewsREEL 2017 challenge.

Název v anglickém jazyce

News Recommender System based on Association Rules @ CLEF NewsREEL 2017

Popis výsledku anglicky

Digital editions of newspapers cause information overflow and users have problems choosing what they want to read. Systems which recommend news articles are suitable to solve such problems. Nevertheless, they face challenges unknown to the systems recommending books or movies such as a frequency of producing the new content. CLEF NewsREEL challenge enables to compare and evaluate news recommendation systems in an online and offline task focused on recommending articles to real users and tuning of algorithms respectively. This paper deals with an approach based on association rules acting as a classifier. In our approach we experimented with settings that allows to reduce the amount of rules used for the classification and increase the performance that is crucial for real recommendations. We evaluated our approach in both tasks of the CLEF NewsREEL 2017 challenge.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

10

Strana od-do

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Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

Dublin

Datum konání akce

11. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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