With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention by effectively exploiting the structural characteristics inherent in scholarly networks. However, these methods often overlook the asymmetric academic influence that is prevalent in scholarly networks when learning graph representations. To address this limitation, this study proposes the Mutual-Influence-Aware Recommendation (MIARec) model, which employs a gravity-based approach to measure the mutual academic influence between scholars and incorporates this influence into the feature aggregation process during message propagation in graph representation learning. Additionally, the model utilizes a multi-channel aggregation method to capture both individual embeddings of distinct single relational sub-networks and their interdependent embeddings, thereby enabling a more comprehensive understanding of the heterogeneous scholarly network. Extensive experiments conducted on real-world datasets demonstrate that the MIARec model outperforms baseline models across three primary evaluation metrics, indicating its effectiveness in scientific paper recommendation tasks.

翻译：随着科学文献的快速扩张，学者们对精准且高质量的论文推荐需求日益增长。在各种推荐方法中，基于图的方法通过有效利用学术网络固有的结构特征而受到关注。然而，这些方法在学习图表示时，常常忽视了学术网络中普遍存在的不对称学术影响力。为应对这一局限，本研究提出了互影响力感知推荐（MIARec）模型。该模型采用基于引力的方法来度量学者间的相互学术影响力，并将此影响力融入图表示学习中消息传播阶段的特征聚合过程。此外，模型采用多通道聚合方法，以捕获不同单关系子网络的独立嵌入及其相互依赖的嵌入，从而实现对异质学术网络更全面的理解。在真实数据集上进行的大量实验表明，MIARec模型在三个主要评估指标上均优于基线模型，证明了其在科学论文推荐任务中的有效性。