Taobao, as the largest online retail platform in the world, provides billions
of online display advertising impressions for millions of advertisers every
day. For commercial purposes, the advertisers bid for specific spots and target
crowds to compete for business traffic. The platform chooses the most suitable
ads to display in tens of milliseconds. Common pricing methods include cost per
mille (CPM) and cost per click (CPC). Traditional advertising systems target
certain traits of users and ad placements with fixed bids, essentially regarded
as coarse-grained matching of bid and traffic quality. However, the fixed bids
set by the advertisers competing for different quality requests cannot fully
optimize the advertisers' key requirements. Moreover, the platform has to be
responsible for the business revenue and user experience. Thus, we proposed a
bid optimizing strategy called optimized cost per click (OCPC) which
automatically adjusts the bid to achieve finer matching of bid and traffic
quality of page view (PV) request granularity. Our approach optimizes
advertisers' demands, platform business revenue and user experience and as a
whole improves traffic allocation efficiency. We have validated our approach in
Taobao display advertising system in production. The online A/B test shows our
algorithm yields substantially better results than previous fixed bid manner.

本研究提出一种优化共享商品在线广告的投标策略（OCPC），该策略可通过自动调整出价以实现广告与流量质量的细粒度匹配，从而优化广告主需求、平台业务收益和用户体验，并提高流量分配效率。在淘宝展示广告系统生产环境中进行的在线 A/B 测试表明，与之前的固定出价方式相比，OCPC 算法的效果显著更好。