Social recommendations have been widely adopted in substantial domains. Recently, graph neural networks (GNN) have been employed in recommender systems due to their success in graph representation learning. However, dealing with the dynamic property of social network data is a challenge. This research presents a novel method that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph. The model aims to capture user preference over time without going through the complexities of a dynamic graph by adding period nodes to define users' long-term and short-term preferences and aggregating assigned edge weights. The model is applied to real-world data to argue its superior performance. Promising results demonstrate the effectiveness of this model.

翻译：社会推荐已被广泛应用于众多领域。近年来，图神经网络（GNN）因其在图表示学习中的成功而被引入推荐系统。然而，处理社交网络数据的动态特性仍是一个挑战。本研究提出了一种新方法，通过将社交网络数据的动态特性融入异构图来实现社会推荐。该模型通过添加周期节点来定义用户的长期与短期偏好，并聚合分配的边权重，从而在不涉及动态图复杂性的前提下捕捉用户随时间的偏好变化。该模型在真实数据上进行了应用，以论证其优越性能。令人鼓舞的结果证明了该模型的有效性。