An approach for recommending relevant articles in news portal based on Doc2Vec
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302I00" target="_blank" >RIV/61988987:17310/22:A2302I00 - isvavai.cz</a>
Výsledek na webu
<a href="https://ieeexplore.ieee.org/document/9939268" target="_blank" >https://ieeexplore.ieee.org/document/9939268</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/AIKE55402.2022.00010" target="_blank" >10.1109/AIKE55402.2022.00010</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
An approach for recommending relevant articles in news portal based on Doc2Vec
Popis výsledku v původním jazyce
News portals are among the most popular websites, and their main goal is to bring the latest news to their readers. Also, it is important to provide relevant content to various types of readers. In this article, we propose an approach for recommending relevant articles on the news portal based on the content of a specific article. The proposed approach is based on Doc2Vec. The main steps of the proposed approach and training of the Doc2Vec model are described. The article also deals with text similarity problems and limitations of the Czech language in the context of recommending relevant articles. For experiment verification of our approach, random articles from the selected news portal were selected. For each article, our approach recommends the most relevant similar articles. Then, the relevant and irrelevant articles were marked. And finally, the ratio of proposed relevant articles for each random article was calculated. The experimental results show the accuracy and relevancy of the proposed approach.
Název v anglickém jazyce
An approach for recommending relevant articles in news portal based on Doc2Vec
Popis výsledku anglicky
News portals are among the most popular websites, and their main goal is to bring the latest news to their readers. Also, it is important to provide relevant content to various types of readers. In this article, we propose an approach for recommending relevant articles on the news portal based on the content of a specific article. The proposed approach is based on Doc2Vec. The main steps of the proposed approach and training of the Doc2Vec model are described. The article also deals with text similarity problems and limitations of the Czech language in the context of recommending relevant articles. For experiment verification of our approach, random articles from the selected news portal were selected. For each article, our approach recommends the most relevant similar articles. Then, the relevant and irrelevant articles were marked. And finally, the ratio of proposed relevant articles for each random article was calculated. The experimental results show the accuracy and relevancy of the proposed approach.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
ISBN
978-1-6654-7120-6
ISSN
2831-7211
e-ISSN
2831-7203
Počet stran výsledku
6
Strana od-do
—
Název nakladatele
IEEE
Místo vydání
—
Místo konání akce
Laguna Hills
Datum konání akce
19. 9. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—