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Title: Enhancing e-commerce recommender system adaptability with online deep controllable Learning-To-Rank
Authors: Zeng, Anxiang
Yu, Han
He, Hualin
Ni, Yabo
Li, Yongliang
Zhou, Jingren
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: 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. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35, 15214-15222.
Project: Alibaba-NTU-AIR2019B1
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.
ISBN: 978-1-57735-866-4
ISSN: 2159-5399
Rights: © 2021 Association for the Advancement of Artificial Intelligence. All Rights Reserved. This paper was published in Proceedings of Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) and is made available with permission of Association for the Advancement of Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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