论文标题
实时招标中用于随机控制的复发神经网络
Recurrent Neural Networks for Stochastic Control in Real-Time Bidding
论文作者
论文摘要
实时拍卖中的竞标可能是一项艰巨的随机控制任务。尤其是如果不足的交付受到强烈的惩罚，并且市场尚不确定。在合理的市场预测下，大多数当前的作品和实施都集中在最佳地发表广告系列。实际实施情况有一个反馈循环，可以调整和强大的预测错误，但据我们所知，没有实施的实施，它使用了市场风险模型并积极预测市场的转变。在实践中解决这样的随机控制问题实际上非常具有挑战性。本文提出了一个基于复发性神经网络（RNN）体系结构的近似解决方案，该解决方案既有效又实用，可在生产环境中实施。 RNN竞标者规定避免错过其目标所需的一切。在购买丢失的印象时，它也故意降低了其目标，这将比未达到的目标要高于罚款。
Bidding in real-time auctions can be a difficult stochastic control task; especially if underdelivery incurs strong penalties and the market is very uncertain. Most current works and implementations focus on optimally delivering a campaign given a reasonable forecast of the market. Practical implementations have a feedback loop to adjust and be robust to forecasting errors, but no implementation, to the best of our knowledge, uses a model of market risk and actively anticipates market shifts. Solving such stochastic control problems in practice is actually very challenging. This paper proposes an approximate solution based on a Recurrent Neural Network (RNN) architecture that is both effective and practical for implementation in a production environment. The RNN bidder provisions everything it needs to avoid missing its goal. It also deliberately falls short of its goal when buying the missing impressions would cost more than the penalty for not reaching it.
