  Video caching has been a basic network functionality in today's network
architectures. Although the abundance of caching replacement algorithms has
been proposed recently, these methods all suffer from a key limitation: due to
their immature rules, inaccurate feature engineering or unresponsive model
update, they cannot strike a balance between the long-term history and
short-term sudden events. To address this concern, we propose LA-E2, a
long-short-term fusion caching replacement approach, which is based on a
learning-aided exploration-exploitation process. Specifically, by effectively
combining the deep neural network (DNN) based prediction with the online
exploitation-exploration process through a \emph{top-k} method, LA-E2 can both
make use of the historical information and adapt to the constantly changing
popularity responsively. Through the extensive experiments in two real-world
datasets, we show that LA-E2 can achieve state-of-the-art performance and
generalize well. Especially when the cache size is small, our approach can
outperform the baselines by 17.5\%-68.7\% higher in total hit rate.
