News Recommendation Model Based on Encoder Graph Neural Network and Bat Optimization in Online Social Multimedia Art Education
Computer Science and Information Systems, Tome 21 (2024) no. 3.

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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.
Keywords: news recommendation system, encoder graph neural network, Bat optimization, online social networks, GRU
@article{CSIS_2024_21_3_a14,
     author = {Jing Yu and Lu Zhao and Shoulin Yin and Mirjana Ivanovic},
     title = {News {Recommendation} {Model} {Based} on {Encoder} {Graph} {Neural} {Network} and {Bat} {Optimization} in {Online} {Social} {Multimedia} {Art} {Education}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {3},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a14/}
}
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Jing Yu; Lu Zhao; Shoulin Yin; Mirjana Ivanovic. News Recommendation Model Based on Encoder Graph Neural Network and Bat Optimization in Online Social Multimedia Art Education. Computer Science and Information Systems, Tome 21 (2024) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a14/