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.