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News Recommendation Model Based on Encoder Graph Neural Network and Bat Optimization in Online Social Multimedia Art Education

Jing Yu¹, Lu Zhao^{1, 4}, Shoulin Yin² and Mirjana Ivanovic³

1. Lu Xun Academy of Fine Arts
   Shenyang 110004, China
   ohyou@sina.com
2. Software College, Shenyang Normal University
   Shenyang 110034, China
   yslin@hit.edu.cn
3. Faculty of Sciences, University of Novi Sad
   21000 Novi Sad, Serbia
   mira@dmi.uns.ac.rs

Abstract

At present, the existing news recommendation system fails to fully consider the semantic information of news, meanwhile, the uneven popularity of news will also cause the phenomenon of long tail. Therefore, we propose a novel news recommendation model based on encoder graph neural network and Bat optimization in online social networks. Firstly, Bat optimization algorithm is used to improve the effect of news clustering. Secondly, the concept of metadata is introduced into the graph neural network, and the ontology of learning resources based on knowledge points is established to realize the correlation between news resources. Finally, the model combining Convolutional Neural Network (CNN) and attention network is used to learn the representation of news, and Gate Recurrent Unit (GRU) is used to learn the short-term preferences of users from their recent reading history. We carry out experiments on real news datasets, and compared with other advanced methods, the proposed model has better evaluation indexes.

Key words

news recommendation system, encoder graph neural network, Bat optimization, online social networks, GRU

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS231225025Y

Publication information

Volume 21, Issue 3 (June 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Yu, J., Zhao, L., Yin, S., Ivanovic, M.: News Recommendation Model Based on Encoder Graph Neural Network and Bat Optimization in Online Social Multimedia Art Education. Computer Science and Information Systems, Vol. 21, No. 3, 989-1012. (2024), https://doi.org/10.2298/CSIS231225025Y