Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser's profits for group-buying ads depends on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts, and dynamically vary bids to maximize profits. Further, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real time ad selection, admissibility, and real time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection and admission strategies on a multi-million click stream of 935 ads. The proposed real-time bidding, selection and admissibility show significant profit increases over the existing strategies. Further the experiments illustrate the robustness of the bidding and acceptable computation timings.
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。
Dual Based DSP Bidding Strategy and its Application
近年来，RTB（实时竞价）成为一种流行的在线广告交易方式。在拍卖过程中，每个DSP（需求方平台）应该评估当前的机会，并与广告和相应的投标价格响应。在P4P（Pay For Performance）或P4U（Pay For Usage）模式下，DSP需要在预算和投资回报率（ROI）约束下找到一个最优的广告选择和竞价策略，以最大限度地提高收入或业绩。我们解决这个问题，1）形式化的DSP问题作为一个约束优化问题，2）提出了增广MMKP（多选择多维背包问题）的一般解决方案，3）和证明DSP问题是一个特殊情况下，增广MMKP和推导专门的战略。我们的策略是通过仿真验证，并在实际应用中优于国家的最先进的战略。据我们所知，我们的解决方案是第一个基于双DSP的投标框架，该框架来自严格的第二价格拍卖假设，通常适用于具有各种目标和约束的多个广告场景。
