With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multi-dimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

中文翻译：

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基于移动边缘计算的以内容为中心的社交网络中基于多头注意力的流行度预测缓存

随着社交网络流量的快速增长，有效的缓存策略的设计对于以社交内容为中心的网络（SocialCCN）至关重要。为了设计更全面的流行度预测缓存策略，在本文中，我们提出了一种在SocialCCN（MeSoCCN）中集成移动边缘计算（MEC）的新颖体系结构，并在MeSoCCN中提出了基于多头注意力的流行度预测缓存策略。首先，我们提出了一种基于多头注意力的流行度预测模型（MAPP），该模型考虑了多维特征，包括历史和未来的流行度，社会关系以及地理位置，以预测内容的流行度。然后，我们根据MAPP的预测结果设计了一种缓存策略。