Online Bidding Algorithms for Return-on-Spend Constrained Advertisers
Abstract
Online advertising has recently become a highly competitive, complex, multi-billion-dollar industry with advertisers bidding for ad slots at large scales and high frequencies. This has resulted in the growing need for efficient ``auto-bidding'' algorithms to determine the bids for incoming queries to maximize advertisers' targets subject to their specified constraints. Our work focuses on designing efficient online algorithms for a single value-maximizing advertiser under a commonly seen constraint: Return-on-Spend (RoS). We quantify the efficiency in terms of \emph{regret} in comparison with the optimal algorithm that knows all the queries a priori.
Our main contribution is an algorithm that achieves near-optimal regret while respecting the specified RoS constraint. We are able to also incorporate our results with the pre-existing work of Balseiro, Lu, and Mirrokni~\cite{BLM20} to achieve near-optimal regret while respecting \emph{both} RoS and fixed budget constraints. Our main technical insight is in leveraging ideas from the literature on (offline) packing linear programs, in addition to the generic structural properties arising from our auction model.
Our main contribution is an algorithm that achieves near-optimal regret while respecting the specified RoS constraint. We are able to also incorporate our results with the pre-existing work of Balseiro, Lu, and Mirrokni~\cite{BLM20} to achieve near-optimal regret while respecting \emph{both} RoS and fixed budget constraints. Our main technical insight is in leveraging ideas from the literature on (offline) packing linear programs, in addition to the generic structural properties arising from our auction model.
Research Areas
