Title: News recommendation optimisation path based on improved GAT algorithm

Authors: Kunlong Yang; Minjing Wang

Addresses: School of Literature and Media, Xi'an FanYi University, Xi'an, 710105, China ' School of Literature and Media, Xi'an FanYi University, Xi'an, 710105, China

Abstract: To solve the problem of recommending massive news information mentioned above, a semantic representation model based on news text information fusion is proposed. The innovation lies in the use of capsule networks and Transformers for feature extraction and fusion, while proposing a perceptual model for graphic attention networks to enhance the capture of relevant information. In the accuracy recall analysis, the research model achieved an accuracy of 0.962 in sports news scenarios, which is superior to the other two models. In the comparison of recommendation accuracy, the model showed the best performance with recommendation accuracy of 0.915 and 0.961 on the Yahoo and MIND datasets, respectively. It can be seen that the research model meets the requirements of news and user development. The research content will provide important technical references for the effective dissemination of news and the improvement of recommendation techniques.

Keywords: GAT; news recommendations; capsule network; transformer; perception model.

DOI: 10.1504/IJCSM.2025.149894

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.2, pp.107 - 128

Received: 29 Oct 2024
Accepted: 26 May 2025
Published online: 17 Nov 2025 *

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