论文标题
用于拍卖中在线学习的实时优化
Real-Time Optimisation for Online Learning in Auctions
论文作者
论文摘要
在展示广告中，一小群卖家和投标人每天最多10 12个拍卖会相互面对。在这种情况下，通过垄断价格学习的收入最大化是卖方的高价值问题。从本质上讲，这些拍卖是在线的，并产生了非常高的数据流。这会导致需要实时算法的计算应变。不幸的是，从批处理设置继承的现有方法会在每个更新中遭受o（$ \ sqrt t $）的时间/内存复杂性，禁止使用它们。在本文中，我们为在线拍卖中的垄断价格在线学习提供了第一种算法，其更新是时间和记忆。
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer O($\sqrt t$) time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.
