  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.
