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Record type:	stať ve sborníku (D)
Home Department:	Katedra informatiky a počítačů (31400)
Title:	An approach for recommending relevant articles in news portal based on Doc2Vec
Citace	Walek, B. a Müller, P. An approach for recommending relevant articles in news portal based on Doc2Vec. In: 2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE): 2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) 2022-09-19 Laguna Hills. IEEE, 2022. ISBN 978-1-6654-7120-6.

Subtitle
Publication year:	2022
Obor:
Number of pages:	6
Page from:	neuvedeno
Page to:	neuvedeno
Form of publication:	Elektronická verze
ISBN code:	978-1-6654-7120-6
ISSN code:	2831-7211
Proceedings title:	2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
Proceedings:
Publisher name:	IEEE
Place of publishing:	neuvedeno
Country of Publication:	Sborník vydaný v zahraničí
Název konference:	2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
Místo konání konference:	Laguna Hills
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Celosvětová akce
WoS code:
EID:	2-s2.0-85143083069

Key words in English:	news portal, recommending, relevant articles, Doc2Vec, data processing, similarity
Annotation in original language:	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.
Annotation in english language:

References

Reference

R01:	RIV/61988987:17310/22:A2302I00

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