# A framework for massive scale personalized promotion

arXiv: https://arxiv.org/abs/2108.12100 · HTML: https://mlsanity.com/paper/2108.12100/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Causal Embeddings for Recommendation](https://mlsanity.com/paper/1706.07639/) (2017)
- [Deep Lattice Networks and Partial Monotonic Functions](https://mlsanity.com/paper/1709.06680/) (2017)
- [Monotonic Calibrated Interpolated Look-Up Tables](https://mlsanity.com/paper/1505.06378/) (2015)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [Counterfactual Prediction with Deep Instrumental Variables Networks](https://mlsanity.com/paper/1612.09596/) (2016)
- [Solving Billion-Scale Knapsack Problems](https://mlsanity.com/paper/2002.00352/) (2020)
- [Latent Dirichlet Allocation for Internet Price War](https://mlsanity.com/paper/1808.07621/) (2018)

## Built on by (most cited first, 4 of 4)

- [Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](https://mlsanity.com/paper/2207.14798/) (2022)
- [MMCE: A Framework for Deep Monotonic Modeling of Multiple Causal Effects](https://mlsanity.com/paper/2504.03753/) (2025)
- [Knowledge-informed Bidding with Dual-process Control for Online Advertising](https://mlsanity.com/paper/2603.04920/) (2026)
- [Estimating the Effect of Timing on Coupon Effectiveness](https://mlsanity.com/paper/2606.30664/) (2026)

## Abstract (arXiv)

Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popular approach for increasing active users on such platforms. On one hand, increased user activities can introduce network effect, bring in advertisement audience, and produce other benefits. On the other hand, massive-scale promotion causes massive cost. Therefore making promotion campaigns efficient in terms of return-on-investment (ROI) is of great interest to many companies. This paper proposes a practical two-stage framework that can optimize the ROI of various massive-scale promotion campaigns. In the first stage, users' personal promotion-response curves are modeled by machine learning techniques. In the second stage, business objectives and resource constraints are formulated into an optimization problem, the decision variables of which are how much incentive to give to each user. In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage. We leverage existing counterfactual prediction techniques to correct treatment bias in data. We also introduce a novel deep neural network (DNN) architecture, the deep-isotonic-promotion-network (DIPN), to reduce noise in the promotion response curves. The DIPN architecture incorporates our prior knowledge of response curve shape, by enforcing isotonicity and smoothness. It out-performed regular DNN and other state-of-the-art shape-constrained models in our experiments.