Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

中文翻译：

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面向内容缓存的流行度预测：一种加权聚类方法

内容流行度预测在主动内容缓存中起着重要作用。与现有的大多数专注于提高流行度预测精度的工作不同，在本文中，我们通过加权聚类方法考虑面向内容缓存的流行度预测，以提高缓存性能。我们将缓存命中率的损失作为系统遗憾来表示缓存性能，并构建基于聚类的流行度预测框架，以克服用户请求稀疏性，同时考虑流行度演变趋势的相似性。为了描述缓存性能和流行度预测精度之间的显式关系，我们推导了每个内容的流行度预测误差分布，并设计了缓存阈值。通过提取流行度预测精度与用户聚类策略之间关系的见解，我们开发了一种基于加权聚类的流行度预测算法，该算法以文件的缓存后悔概率为权重。基于两个真实世界的数据集，模拟结果表明，所提出的流行度预测方案比最先进的方案实现了更好的缓存性能。