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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19078 |
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| _version_ | 1866910544809689088 |
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| author | Chen, Bobby Chen, Siyu Dowlatabadi, Jason Hong, Yu Xuan Iyer, Vinayak Mantripragada, Uday Narang, Rishabh Pandey, Apoorv Qin, Zijun Sheikh, Abrar Sun, Hongtao Sun, Jiaqi Walker, Matthew Wei, Kaichen Xu, Chen Yang, Jingnan Zhang, Allen T. Zhang, Guoqing |
| author_facet | Chen, Bobby Chen, Siyu Dowlatabadi, Jason Hong, Yu Xuan Iyer, Vinayak Mantripragada, Uday Narang, Rishabh Pandey, Apoorv Qin, Zijun Sheikh, Abrar Sun, Hongtao Sun, Jiaqi Walker, Matthew Wei, Kaichen Xu, Chen Yang, Jingnan Zhang, Allen T. Zhang, Guoqing |
| contents | Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning Chen, Bobby Chen, Siyu Dowlatabadi, Jason Hong, Yu Xuan Iyer, Vinayak Mantripragada, Uday Narang, Rishabh Pandey, Apoorv Qin, Zijun Sheikh, Abrar Sun, Hongtao Sun, Jiaqi Walker, Matthew Wei, Kaichen Xu, Chen Yang, Jingnan Zhang, Allen T. Zhang, Guoqing Machine Learning 62J99 Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency. |
| title | Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning |
| topic | Machine Learning 62J99 |
| url | https://arxiv.org/abs/2407.19078 |