选择性呼叫和实时的出价
Selective Call Out and Real Time Bidding
摘要：
互联网上的广告越来越多地通过广告交易所出售，如RightMedia，AdECN和Doubleclick Ad Exchange。这些交易所允许实时竞价，也就是说，每次发布者联系交易所时，交易所都会“呼叫”广告网络以征求出价。这方面的招标介绍了一个新的方面，在现有的文献。这就建议制定一个联合优化框架，对分配和招标进行优化。我们将这种选择性呼叫建模为具有带宽类型约束的在线循环贝叶斯决策框架。我们得到自然算法有界的性能保证几个自然的优化标准。我们表明，这些结果保持在不同的呼叫约束模型，和不同的到达过程。有趣的是，本文表明，在MHR假设下，广义第二价格拍卖的期望收益是期望福利的常数因子。此外，本文的分析允许我们证明adwords问题的适应性缺口类型结果。
英文摘要：
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.
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