Graph Convolutional News Recommendation Model Based on Temporal Features

doi:10.3778/j.issn.1673-9418.2501019

Abstract

Abstract: To address the issue that existing news recommendation systems fail to effectively extract user interaction data and reading habit information in dynamic feature extraction, this paper proposes a time-aware graph convolutional network model, TimelyGCN. This model comprehensively considers user dwell time, the temporal characteristics of reading behavior, and the lifecycle attributes of news articles to capture the evolution of user interests and the freshness changes of news content. During the news feature extraction phase, reading duration data are incorporated, and temporal modeling along with semantic analysis is used to deeply explore the dynamic relationship between users and news content. Furthermore, this paper constructs a user-news interaction graph and integrates behavior time-series features and news lifecycle attributes as composite weights into the graph structure during the pretraining phase, explicitly modeling the temporal dependencies of user interests, thereby enhancing the model’s adaptability to news consumption patterns. In the recommendation prediction phase, residual lifecycle information of news articles is further leveraged, and a dynamic weight adjustment mechanism is employed to optimize the exposure priority of candidate news, balancing personalized recommendations with the timeliness of news content, thus improving the accuracy and timeliness of recommendations. Experimental results demonstrate that the proposed model significantly enhances recommendation performance on the Adressa dataset, achieving improvements across various metrics, including AUC, MRR, nDCG@5, and nDCG@10, compared with existing baseline methods.

Key words: graph convolutional network, interaction graph, temporal awareness, dynamic preferences

摘要： 针对现有新闻推荐系统在动态特征提取中未能有效挖掘用户交互数据与阅读习惯信息的问题，提出一种基于时间特征的图卷积网络模型TimelyGCN。该模型充分考虑用户停留时长、阅读行为的时序特征以及新闻的生命周期属性，以刻画用户兴趣的演化和新闻内容的新鲜度变化。在新闻特征提取阶段，引入阅读时长数据，并结合时态建模与语义分析，深度挖掘用户与新闻内容的动态关联。同时，构建用户-新闻交互图，并在预训练阶段将行为时间序列特征与新闻生命周期属性作为复合权重融入图结构中，以显式建模用户兴趣的时间依赖性，从而增强模型对新闻消费模式的适应能力。在推荐预测阶段，进一步结合新闻的剩余生命周期信息，采用动态权重调控机制优化候选新闻的曝光优先级，以平衡个性化推荐与新闻内容时效性之间的关系，从而提升推荐效果的精准度和实时性。实验结果显示，提出的模型在Adressa数据集上有效提升了推荐性能，相较于现有基线网络方法，在AUC、MRR、nDCG@5和nDCG@10指标上均取得了不同程度的提升。

关键词: 图卷积网络, 交互图, 时间感知, 动态偏好

YANG Zhiyong, CHEN Jiahui, XU Qinxin. Graph Convolutional News Recommendation Model Based on Temporal Features[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(11): 3059-3071.

杨智勇, 陈佳慧, 许沁欣. 基于时间特征的图卷积新闻推荐模型[J]. 计算机科学与探索, 2025, 19(11): 3059-3071.

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