Title: Optimized Cost per Click in Taobao Display Advertising,
Abstract: 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.