Article: A news content recommendation model integrating social relationships and temporal features Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.37 pp.59 - 74 Abstract: Aiming at the problems of dynamic changes of user interests and underutilisation of social influence in current news recommendation, this paper proposes a social-temporal enhanced news recommendation model (STENR) that integrates social relations and temporal features. The model uses graph neural network (GNN) to model the user-user social relationship graph, and adopts transformer encoder to capture temporal dependencies and generate temporal embeddings reflecting the dynamics of users' recent interests. At the same time, a text encoder is used to extract the deep semantic features of the news content. The user's comprehensive interest representation is generated dynamically by weighting the fusion information adaptively through the attention mechanism. Experiments show that the AUC of STENR is increased to 0.812, the length of user stay is increased by 23.4%, and the social conversion rate is increased by 15.3%, which verifies its academic validity and industrial value. Inderscience Publishers - linking academia, business and industry through research

Title: A news content recommendation model integrating social relationships and temporal features

Authors: Xiaofeng Liang

Addresses: School of Media and Design, Xi'an Peihua University, Xi'an 710199, China

Abstract: Aiming at the problems of dynamic changes of user interests and underutilisation of social influence in current news recommendation, this paper proposes a social-temporal enhanced news recommendation model (STENR) that integrates social relations and temporal features. The model uses graph neural network (GNN) to model the user-user social relationship graph, and adopts transformer encoder to capture temporal dependencies and generate temporal embeddings reflecting the dynamics of users' recent interests. At the same time, a text encoder is used to extract the deep semantic features of the news content. The user's comprehensive interest representation is generated dynamically by weighting the fusion information adaptively through the attention mechanism. Experiments show that the AUC of STENR is increased to 0.812, the length of user stay is increased by 23.4%, and the social conversion rate is increased by 15.3%, which verifies its academic validity and industrial value.

Keywords: news recommendation; social relationship modelling; temporal feature extraction; graph neural network; GNN.

DOI: 10.1504/IJICT.2025.149178

International Journal of Information and Communication Technology, 2025 Vol.26 No.37, pp.59 - 74

Received: 11 Jul 2025
Accepted: 29 Aug 2025
Published online: 16 Oct 2025 *

Title: A news content recommendation model integrating social relationships and temporal features

Keep up-to-date