WebJul 12, 2024 · In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. WebNews recommendation, Graph neural networks, Long-term interest, Short-term interest 1. Introduction As the amount of online news platforms such as Yahoo! news1 and Google news2 increases, users are overwhelmed with a large volume of news from the worldwide covering various topics. To alleviate the information overloading,
Temporal sensitive heterogeneous graph neural network for news ...
WebInteraction graph neural network for news recommendation. In Proceedings of the International Conference on Web Information Systems Engineering. Springer, 599 – 614. Google Scholar [37] Qiu Ruihong, Huang Zi, Li Jingjing, and Yin Hongzhi. 2024. Exploiting cross-session information for session-based recommendation with graph neural … WebOct 29, 2024 · In this paper, we propose a new news recommendation model, Interaction Graph Neural Network (IGNN), which integrates a user-item interactions graph and a … how do you get rid of redness from a pimple
Attention-Based Graph Neural Network for News …
WebSep 7, 2024 · GNewsRec considering the sparsity of the user-news interaction graph, extracted the topics of the news as the connection among news to enrich the networks. ... Therefore, a novel graph neural network based recommendation method, FigGNN, is proposed in this paper to explore fine-grained user preferences for the … WebJul 22, 2024 · Therefore, we propose an attention-based graph neural network news recommendation model. In our model, muti-channel convolutional neural network is used to generate news representations, and recurrent neural network is used to extract the news sequence information that users clicked on. WebOct 30, 2024 · In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user's interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations ... phoenyx games cabal