Enhancing E-commerce Recommender System Adaptability with Online Deep Controllable Learning-To-Rank

Authors

  • Anxiang Zeng Nanyang Technological University Alibaba-NTU Singapore Joint Research Institute
  • Han Yu Nanyang Technological University
  • Hualin He Alibaba
  • Yabo Ni Alibaba
  • Yongliang Li Alibaba
  • Jingren Zhou Alibaba
  • Chunyan Miao Nanyang Technological University Alibaba-NTU Singapore Joint Research Institute

Keywords:

Learning To Rank, E-commerce, Online Learning, Multi-objective Optimization

Abstract

In the past decade, recommender systems for e-commerce have witnessed significant advancement. Recommendation scenarios can be divided into different type (e.g., pre-, during-, post-purchase, campaign, promotion, bundle) for different user groups or different businesses. For different scenarios, the goals of recommendation are different. This is reflected by the different performance metrics employed. In addition, online promotional campaigns, which attract high traffic volumes, are also a critical factor affecting e-commerce recommender systems. Typically, prior to a promotional campaign, the Add-to-Cart Rate (ACR) is the target of optimization. During the campaign, this changes to Gross Merchandise Volumes (GMV). Immediately after the campaign, it becomes Click Through Rates CTR. Dynamically adapting among these potentially conflicting optimization objectives is an important capability for recommender systems deployed in real-world e-commerce platforms. In this paper, we report our experience designing and deploying the Deep Controllable Learning-To-Rank (DC-LTR) recommender system to address this challenge. It enhances the feedback controller in LTR with multi-objective optimization so as to maximize different objectives under constraints. Its ability to dynamically adapt to changing business objectives has resulted in significant business advantages. Since September 2019, DC-LTR has become a core service enabling adaptive online training and real-time deployment ranking models based on changing business objectives in AliExpress and Lazada. Under both everyday use scenarios and peak load scenarios during large promotional campaigns, DC-LTR has achieved significant improvements in satisfying real-world business objectives.

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Published

2021-05-18

How to Cite

Zeng, A., Yu, H., He, H., Ni, Y., Li, Y., Zhou, J., & Miao, C. (2021). Enhancing E-commerce Recommender System Adaptability with Online Deep Controllable Learning-To-Rank. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15214-15222. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17785

Issue

Section

IAAI Technical Track on Highly Innovative Applications of AI