A framework for massive scale personalized promotion
Yitao Shen, Yue Wang, Xingyu Lu, Feng Qi, Jia Yan, Yixiang Mu, Yao, Yang, YiFan Peng, Jinjie Gu

TL;DR
This paper introduces a two-stage framework utilizing machine learning and optimization to maximize ROI in massive-scale personalized promotion campaigns, incorporating novel neural network architecture for response modeling.
Contribution
It presents a new deep neural network architecture, DIPN, for accurate response curve modeling and a comprehensive framework combining counterfactual prediction with optimization for large-scale promotion.
Findings
DIPN outperforms standard DNN and shape-constrained models.
The framework effectively balances promotion costs and benefits.
Counterfactual correction improves response prediction accuracy.
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Advanced Bandit Algorithms Research · Consumer Market Behavior and Pricing
