TL;DR
This paper introduces LBCF, a scalable tree-based algorithm for optimal incentive allocation under budget constraints, demonstrating significant improvements in real-world large-scale platform applications.
Contribution
The paper presents a novel large-scale, budget-constrained causal forest algorithm and an offline evaluation method for effective treatment selection in RCT data.
Findings
Outperforms existing tree-based methods in simulations and real-world tests
Serves hundreds of millions of users with improved ROI
Achieves significant performance gains over baseline algorithms
Abstract
Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effectiveness, these marketing incentives incur an inevitable cost and might result in a low ROI (Return on Investment) if not used properly. On the other hand, different users respond differently to these incentives, for instance, some users never buy certain products without coupons, while others do anyway. Thus, how to select the right amount of incentives (i.e. treatment) to each user under budget constraints is an important research problem with great practical implications. In this paper, we call such problem as a budget-constrained treatment selection (BTS) problem. The challenge is how to efficiently solve BTS problem on a Large-Scale dataset and achieve improved…
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