Multi-feature content popularity prediction algorithm based on GRU-Attention in V-NDN

The Vehicle Named Data Networking(V-NDN) is a vehicular ad-hoc network with the Named Data Networking(NDN) as the architecture, and the most advantageous feature is the in-network cache, which caches the content in the intermediate nodes of the network and can quickly satisfy the requests of subsequent consumers for the same content. Since the cache space of nodes is limited, the cached content should be the popular content frequently requested by users in the network, so the most important problem is accurately finding out the future popular content in the network. This paper designs a multi-feature content popularity prediction algorithm to address this problem based on the attention mechanism and GRU (GRU-Attention). According to the characteristics of multiple historical requests for content, the GRU-Attention model is used to predict the future popularity of content. Through experimental verification, the content popularity prediction algorithm proposed in this paper effectively improves the accuracy of prediction.