The surging demand for high-definition video streaming services and large neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a tremendous explosion of Internet traffic. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users. Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method. In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieve remarkable performance. However, DGNN models still suffer from tackling sparse datasets where most users are inactive. Therefore, we propose a reformative temporal graph network, named semantics-enhanced temporal graph network (STGN), which attaches extra semantic information into the user-content bipartite graph and could better leverage implicit relationships behind the superficial topology structure. On top of that, we customize its temporal and structural learning modules to further boost the prediction performance. Specifically, in order to efficiently aggregate the diversified semantics that a content might possess, we design a user-specific attention (UsAttn) mechanism for temporal learning module. Unlike the attention mechanism that only analyzes the influence of genres on content, UsAttn also considers the attraction of semantic information to a specific user. Meanwhile, as for the structural learning, we introduce the concept of positional encoding into our attention-based graph learning and adopt a semantic positional encoding (SPE) function to facilitate the analysis of content-oriented user-association analysis. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the effectiveness in content caching.

翻译：高清视频流媒体服务及大型神经网络模型（例如，生成式预训练Transformer，GPT）需求的激增导致了互联网流量的巨大爆炸。为缓解流量压力，已提出具有网络内部存储的架构，将流行内容缓存到更靠近用户的设备中。相应地，为最大化缓存利用率，设计有效的流行度预测方法变得至关重要。在这方面，使用动态图神经网络（DGNN）模型进行流行度预测取得了显著性能。然而，DGNN模型在处理大多数用户不活跃的稀疏数据集时仍存在困难。因此，我们提出了一种改进的时间图网络，名为语义增强的时间图网络（STGN），它将额外的语义信息附加到用户-内容二分图中，并能更好地利用表面拓扑结构后的隐含关系。在此基础上，我们自定义了其时序和结构学习模块，以进一步提升预测性能。具体而言，为有效聚合内容可能拥有的多样化语义，我们为时序学习模块设计了用户特定注意力（UsAttn）机制。与仅分析类型对内容影响的注意力机制不同，UsAttn还考虑了语义信息对特定用户的吸引力。同时，在结构学习方面，我们将位置编码的概念引入基于注意力的图学习中，并采用语义位置编码（SPE）函数来促进面向内容的用户关联分析。最后，大量仿真验证了我们STGN模型的优越性，并证明了其在内容缓存中的有效性。