基于注意力机制的Bi-GRU内容流行度预测算法

Bi-GRU content popularity prediction algorithm based on attention mechanism

内容资源流行度预测是内容分发网络提高缓存与调度效率的主要依据之一。针对当前流行度预测算法特征表征能力和适应性较差,准确率低等不足,提出一种基于深度学习的内容资源流行度预测算法,该算法基于融合注意力机制的双向GRU模型可以更好地挖掘资源访问历史中蕴含的信息及其相关性,提高特征提取的效率和质量,并具有更为包容的泛化能力。相关不同数据集上的实验结果表明该算法各项指标均优于已有的多种主流算法,且准确率高达96.20%和98.03%。

Content resource popularity prediction is one of the main basis for content delivery network to improve the efficiency of caching and scheduling.In view of the poor feature representation ability,adaptability and low accuracy of current popularity prediction algorithms,this paper proposes a content resource popularity prediction algorithm based on deep learning.The algorithm is better based on a two-way GRU model of the fusion attention mechanism,which can better mine the information contained in the resource access history and its correlation,improve the efficiency and quality of feature extraction,and has a more tolerant generalization ability.The experimental results on different data sets show that the various indicators of the algorithm are better than the existing mainstream algorithms,and the accuracy rates are as high as 96.20%and 98.03%.