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- W4290927921 abstract "Speed of delivery is critical for the success of e-commerce platforms. Faster delivery promise to the customer results in increased conversion and revenue. There are typically two mechanisms to control the delivery speed - a) replication of products across warehouses, and b) air-shipping the product. In this paper, we present a machine learning based framework to recommend air-shipping eligibility for products. Specifically, we develop a causal inference framework (referred to as Air Shipping Recommendation or ASPIRE) that balances the trade-off between revenue or conversion and delivery cost to decide whether a product should be shipped via air. We propose a doubly-robust estimation technique followed by an optimization algorithm to determine air eligibility of products and calculate the uplift in revenue and shipping cost." @default.
- W4290927921 created "2022-08-13" @default.
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- W4290927921 date "2022-08-14" @default.
- W4290927921 modified "2023-10-16" @default.
- W4290927921 title "ASPIRE" @default.
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- W4290927921 doi "https://doi.org/10.1145/3534678.3539197" @default.
- W4290927921 hasPublicationYear "2022" @default.
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