Ads on the Internet are increasingly sold via ad exchanges such as
RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time
bidding, that is, each time the publisher contacts the exchange, the exchange
``calls out'' to solicit bids from ad networks. This aspect of soliciting bids
introduces a novel aspect, in contrast to existing literature. This suggests
developing a joint optimization framework which optimizes over the allocation
and well as solicitation. We model this selective call out as an online
recurrent Bayesian decision framework with bandwidth type constraints. We
obtain natural algorithms with bounded performance guarantees for several
natural optimization criteria. We show that these results hold under different
call out constraint models, and different arrival processes. Interestingly, the
paper shows that under MHR assumptions, the expected revenue of generalized
second price auction with reserve is constant factor of the expected welfare.
Also the analysis herein allow us prove adaptivity gap type results for the
adwords problem.

该论文提出了一个联合优化框架，用于协调互联网广告竞价和售卖中存在的挑选投标的因素，运用一种基于贝叶斯决策模型的方法进行了模型建立以及性能上界的分析，最终得到了该方法在几种不同情况下的性能保障，其中也包括了针对广告拍卖的一些启发式算法。