Real-Time Optimisation for Online Learning in Auctions
This addresses a high-value computational bottleneck for sellers in display advertising, enabling real-time optimization in high-frequency auction environments.
The paper tackles the problem of real-time revenue maximization via monopoly price learning in online auctions, where existing methods have O(√t) complexity, and presents an algorithm with constant time and memory per update.
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.