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.
翻译:建立以消费者为主的平台的技术公司可能有机会接触大规模用户人口。近年来,以量化激励促进办法已成为在这类平台上增加积极用户的流行做法。一方面,增加用户活动可引入网络效应,吸引广告受众,并产生其他好处。另一方面,大规模促进活动可造成巨额成本。因此,使宣传活动在回报投资方面效率高(ROI)对于许多公司来说非常重要。本文件提出了一个实用的两阶段框架,可以优化各种大规模促销运动的ROI。在第一阶段,用户的个人促进-反应曲线以机器学习技术为模型。在第二阶段,将业务目标和资源限制发展成一个优化问题,决定变量是给每个用户多少动力。为了在第二阶段有效优化,反事实预测和减少噪音对第一阶段至关重要。我们利用现有的反事实预测技术来纠正数据中的治疗偏差。我们还引入了一个新型的深神经网络架构,以机器学习技术为模型模拟了用户的个人促进-反应曲线。在第二阶段,将业务目标和资源限制形成一个优化的问题,决定变量是给每个用户提供多少动力。为了在第二阶段进行有效的优化,反景象预测和降低我们国家变压结构中的升级反应。通过先变压式的模型来减少我们的国家变压。