Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm

  • Journal/Conference Proceedings
    IJCAI AI Application in E-Commerce workshop
  • Publication Time
    2017
  • Authors
    Yan Yan, Wentao Guo, Meng Zhao, Jinghe Hu, and Weipeng Yan.
We propose a dynamic ranking paradigm, named as DNN-MAB, that is composed of a pairwise deep neural network (DNN) pre-ranker connecting a revised multi-armed bandit (MAB) dynamic 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 pre-ranking scores to assist customers locating items they are interested in most so that they can converge quickly and frequently.