In online advertising (Ad), advertisers are always eager to know how to globally optimize their budget allocation strategies across different channels for more conversions such as orders, payments, etc. Ignoring competition among different advertisers causes objective inconsistency, that is, a single advertiser locally optimizes the conversions only based on its own historical statistics, which is far behind the global conversions maximization. In this paper, we present a cross-channel Advertising Coordinated budget allocation framework (AdCob) to globally optimize the budget allocation strategy for overall conversions maximization. We are the first to provide deep insight into modeling the competition among different advertisers in cross-channel budget allocation problems. The proposed iterative algorithm combined with entropy constraint is fast to converge and easy to implement in large-scale online Ad systems. Both results from offline experiments and online A/B budget bucketing experiments demonstrate the effectiveness of AdCob.

翻译：在在线广告中，广告主始终渴望了解如何跨不同渠道全局优化其预算分配策略，以获取更多转化（如订单、支付等）。忽视不同广告主之间的竞争会导致目标不一致，即单个广告主仅基于自身历史统计数据进行局部转化优化，这远未达到全局转化最大化。本文提出了一种跨渠道广告协调预算分配框架（AdCob），以全局优化预算分配策略，实现整体转化最大化。我们首次深入揭示了跨渠道预算分配问题中不同广告主之间竞争的建模方法。所提出的结合熵约束的迭代算法收敛迅速，易于在大规模在线广告系统中实现。离线实验和在线A/B预算分桶实验的结果均证明了AdCob的有效性。