  With the transition from people's traditional `brick-and-mortar' shopping to
online mobile shopping patterns in web 2.0 $\mathit{era}$, the recommender
system plays a critical role in E-Commerce and E-Retails. This is especially
true when designing this system for more than $\mathbf{236~million}$ daily
active users. Ranking strategy, the key module of the recommender system, needs
to be precise, accurate, and responsive for estimating customers' intents. We
propose a dynamic ranking paradigm, named as DNN-MAB, that is composed of a
pairwise deep neural network (DNN) $\mathit{pre}$-ranker connecting a revised
multi-armed bandit (MAB) dynamic $\mathit{post}$-ranker. By taking into account
of explicit and implicit user feedbacks such as impressions, clicks,
conversions, etc. DNN-MAB is able to adjust DNN $\mathit{pre}$-ranking scores
to assist customers locating items they are interested in most so that they can
converge quickly and frequently. To the best of our knowledge, frameworks like
DNN-MAB have not been discussed in the previous literature to either E-Commerce
or machine learning audiences. In practice, DNN-MAB has been deployed to
production and it easily outperforms against other state-of-the-art models by
significantly lifting the gross merchandise volume (GMV) which is the objective
metrics at JD.
