research-article

E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling

Authors:

Dmitri GoldenbergAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2863 - 2872

https://doi.org/10.1145/3511808.3557100

Published: 17 October 2022 Publication History

Abstract

Promotions and discounts are essential components of modern e-commerce platforms, where they are often used to incentivize customers towards purchase completion. Promotions also affect revenue and may incur a monetary loss that is often limited by a dedicated promotional budget. We propose an Online Constrained Multiple-Choice Promotions Personalization framework, driven by causal incremental estimations achieved by uplift modeling. Our work formalizes the problem as an Online Multiple-Choice Knapsack Problem and extends the existent literature by addressing cases with negative weights and values as a result from causal estimations. Our real-time adaptive method guarantees budget constraints compliance achieving above 99.7% of the potential optimal impact on various datasets. It was deployed in a large-scale experimental study at Booking.com - one of the leading online travel platforms in the world. The application resulted in 162% improvement in sales while complying a zero-budget constraint, enabling long-term self-sponsored promotional campaigns.

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Cited By

Liu XFan ZDing YGuo YZhang LLi CKong DLi HShen WLarson K(2024)Optimal auction design with user coupons in advertising systemsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/322(2904-2912)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/322
Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Zhou HHuang RLi SJiang GZheng JCheng BLin WBaeza-Yates RBonchi F(2024)Decision Focused Causal Learning for Direct Counterfactual Marketing OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672353(6368-6379)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672353
Show More Cited By

Index Terms

E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization
2. Software and its engineering
  1. Software creation and management
    1. Software development process management

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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October 17 - 21, 2022

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Cited By

Liu XFan ZDing YGuo YZhang LLi CKong DLi HShen WLarson K(2024)Optimal auction design with user coupons in advertising systemsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/322(2904-2912)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/322
Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Zhou HHuang RLi SJiang GZheng JCheng BLin WBaeza-Yates RBonchi F(2024)Decision Focused Causal Learning for Direct Counterfactual Marketing OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672353(6368-6379)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672353
Sverdrup EWu HAthey SWager S(2024)Qini Curves for Multi-Armed Treatment RulesJournal of Computational and Graphical Statistics10.1080/10618600.2024.2418820(1-24)Online publication date: 24-Oct-2024
https://doi.org/10.1080/10618600.2024.2418820
Uehara YNishimura NLi YYang JJobson DOhashi KMatsumoto TSukegawa NTakano Y(2024)Robust portfolio optimization model for electronic coupon allocationINFOR: Information Systems and Operational Research10.1080/03155986.2024.238649462:4(646-660)Online publication date: 14-Aug-2024
https://doi.org/10.1080/03155986.2024.2386494
Chen JXiong JChen GLiu XYan PJiang H(2024)Optimal instant discounts of multiple ride options at a ride-hailing aggregatorEuropean Journal of Operational Research10.1016/j.ejor.2023.10.019314:2(718-734)Online publication date: Apr-2024
https://doi.org/10.1016/j.ejor.2023.10.019
Zhang HNiu LZheng ZZhang ZGu SWu FYu CXu JChen GZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Personalized Automated Bidding Framework for Fairness-aware Online AdvertisingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599765(5544-5553)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599765
Wang CShi XXu SWang ZFan ZFeng YYou AChen YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Multi-stage Framework for Online Bonus Allocation Based on Constrained User Intent DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599764(5028-5038)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599764

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