近似预测投标的收益优化
Revenue Optimization with Approximate Bid Predictions
摘要：
在广告拍卖的背景下，找到好的底价是一个众所周知的挑战性学习问题。这是由于广告机会类型的异质性和目标函数的非凸性。在这项工作中，我们将展示如何减少保留价优化的预测下平方损失，在学习社区中一个很好理解的问题的标准设置。我们进一步根据预测器的平均损失限制了预期出价和收入之间的差距。这是第一个将获得的收入与标准机器学习模型的质量正式联系起来的结果。
英文摘要：
In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
