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A Practical Deep Online Ranking System in E-commerce Recommendation

  • Yan YanEmail author
  • Zitao Liu
  • Meng Zhao
  • Wentao Guo
  • Weipeng P. Yan
  • Yongjun Bao
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

User online shopping experience in modern e-commerce websites critically relies on real-time personalized recommendations. However, building a productionized recommender system still remains challenging due to a massive collection of items, a huge number of online users, and requirements for recommendations to be responsive to user actions. In this work, we present our relevant, responsive, and scalable deep online ranking system (DORS) that we developed and deployed in our company. DORS is implemented in a three-level architecture which includes (1) candidate retrieval that retrieves a board set of candidates with various business rules enforced; (2) deep neural network ranking model that takes advantage of available user and item specific features and their interactions; (3) multi-arm bandits based online re-ranking that dynamically takes user real-time feedback and re-ranks the final recommended items in scale. Given a user as a query, DORS is able to precisely capture users’ real-time purchasing intents and help users reach to product purchases. Both offline and online experimental results show that DORS provides more personalized online ranking results and makes more revenue.

Keywords

Recommender system E-commerce Deep learning Multi-arm bandits 
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Supplementary material

473908_1_En_12_MOESM1_ESM.pdf (308 kb)
Supplementary material 1 (pdf 307 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yan Yan
    • 1
    Email author
  • Zitao Liu
    • 2
  • Meng Zhao
    • 1
  • Wentao Guo
    • 1
  • Weipeng P. Yan
    • 1
  • Yongjun Bao
    • 1
  1. 1.Intelligent Advertising LabJD.COMMountain ViewUSA
  2. 2.TAL AI LabTAL Education GroupBeijingChina

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