Recommending News Articles using Rule-based Classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F17%3A00314021" target="_blank" >RIV/68407700:21240/17:00314021 - isvavai.cz</a>
Result on the web
<a href="https://daz2017.kiv.zcu.cz/data/DaZ2017-Sbornik-final.pdf" target="_blank" >https://daz2017.kiv.zcu.cz/data/DaZ2017-Sbornik-final.pdf</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Recommending News Articles using Rule-based Classifier
Original language description
In this paper we summarize our experiments with a rule-based classifier 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 recommendations.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Data a znalosti 2017
ISBN
978-80-261-0720-0
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
51-55
Publisher name
Západočeská univerzita v Plzni
Place of publication
Plzeň
Event location
Plzeň
Event date
Oct 5, 2017
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—