1 research outputs found
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
With the transition from people's traditional `brick-and-mortar' shopping to
online mobile shopping patterns in web 2.0 , the recommender
system plays a critical role in E-Commerce and E-Retails. This is especially
true when designing this system for more than 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) -ranker connecting a revised
multi-armed bandit (MAB) dynamic -ranker. By taking into account
of explicit and implicit user feedbacks such as impressions, clicks,
conversions, etc. DNN-MAB is able to adjust DNN -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.Comment: 7 pages, 7 figures, accepted by 'IJCAI-17 Workshop AI Applications in
E-Commerce