Online Allocation Rules in Display Advertising

TL;DR

This paper presents a risk-minimization framework for online impression allocation in display advertising with budget constraints, improving revenue and budget balance without assuming specific impression arrival distributions.

Contribution

Introduces a novel risk-based framework for online ad allocation that dynamically updates dual prices to optimize revenue and budget delivery.

Findings

Kullback-Leibler divergence risk measure outperforms others

Framework adapts to demand and budget changes effectively

Empirical results show improved revenue and balance

Abstract

Efficient allocation of impressions to advertisers in display advertising has a significant impact on advertisers' utility and the browsing experience of users. The problem becomes particularly challenging in the presence of advertisers with limited budgets as this creates a complex interaction among advertisers in the optimal impression assignment. In this paper, we study online impression allocation in display advertising with budgeted advertisers. That is, upon arrival of each impression, cost and revenue vectors are revealed and the impression should be assigned to an advertiser almost immediately. Without any assumption on the distribution/arrival of impressions, we propose a framework to capture the risk to the ad network for each possible allocation; impressions are allocated to advertisers such that the risk of ad network is minimized. In practice, this translates to starting with an initial estimate of dual prices and updating them according to the belief of the ad network toward the future demand and remaining budgets. We apply our algorithms to a real data set, and we empirically show that Kullback-Leibler divergence risk measure has the best performance in terms of revenue and balanced budget delivery.