An approach for recommending relevant articles in news portal based on Doc2Vec

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

An approach for recommending relevant articles in news portal based on Doc2Vec

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Article name in the collection

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

Number of pages

6

Pages from-to

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Publisher name

IEEE

Place of publication

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Event location

Laguna Hills

Event date

Sep 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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