Graph Neural Networks have significantly advanced research in recommender systems over the past few years. These methods typically capture global interests using aggregated past interactions and rely on static embeddings of users and items over extended periods of time. While effective in some domains, these methods fall short in many real-world scenarios, especially in finance, where user interests and item popularity evolve rapidly over time. To address these challenges, we introduce a novel extension to Light Graph Convolutional Network (LightGCN) designed to learn temporal node embeddings that capture dynamic interests. Our approach employs causal convolution to maintain a forward-looking model architecture. By preserving the chronological order of user-item interactions and introducing a dynamic update mechanism for embeddings through a sliding window, the proposed model generates well-timed and contextually relevant recommendations. Extensive experiments on a real-world dataset from BNP Paribas demonstrate that our approach significantly enhances the performance of LightGCN while maintaining the simplicity and efficiency of its architecture. Our findings provide new insights into designing graph-based recommender systems in time-sensitive applications, particularly for financial product recommendations.

翻译：图神经网络在过去几年显著推动了推荐系统领域的研究进展。这些方法通常通过聚合历史交互记录来捕捉全局兴趣，并依赖用户和项目在长期内的静态嵌入表示。尽管在某些领域表现有效，这些方法在许多现实场景中仍存在不足，尤其是在金融领域——用户的兴趣偏好与项目的流行度往往随时间快速演变。为应对这些挑战，我们提出一种针对Light图卷积网络（LightGCN）的新型扩展方案，旨在学习能够捕捉动态兴趣的时序节点嵌入。该方法采用因果卷积构建具有前瞻性的模型架构。通过保持用户-项目交互的时间顺序，并借助滑动窗口引入嵌入的动态更新机制，所提出的模型能够生成时效性强且上下文相关的推荐。在法国巴黎银行真实数据集上的大量实验表明，我们的方法在保持LightGCN架构简洁性与高效性的同时，显著提升了其性能表现。本研究为在时间敏感型应用中设计基于图的推荐系统提供了新的思路，尤其对金融产品推荐具有重要参考价值。