Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency

Goldenberg, Dmitri; Tsype, Guy; Spivak, Igor; Albert, Javier; Tzur, Amir

doi:10.1145/3442442.3452051

Cited by 5 publications

(1 citation statement)

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“…The proposed solution presents a dynamic system, similar to the Online Knapsack problem [18], that can decide whether a promotion should be assigned to a customer based on the CATE estimations. It allows dynamic calibration based on the overall measured impact without harming the experience of an individual customer [9]. This framework addresses the fact that a promotional offer can result in both a positive and negative incremental net revenue loss and thus introduces a knapsack problem with negative weights.…”

Section: Introductionmentioning

confidence: 99%

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

Albert¹,

Goldenberg²

2021

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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 study the Online Constrained Multiple-Choice Promotions Personalization Problem, where the optimization goal is to select for each customer which promotion to present in order to maximize purchase completions, while also complying with global budget limitations. 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. We provide a real-time adaptive method that guarantees budget constraints compliance and achieves above 99.7% of the optimal promotional impact on various datasets. Our method is evaluated on a large-scale experimental study at Booking.comone of the leading online travel platforms in the world.

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Section: Introductionmentioning

confidence: 99%