Yield Optimization of Display Advertising with Ad Exchange
Venue
ACM Conference on Electronic Commerce (2011)
Publication Year
2011
Authors
Santiago Balseiro, Jon Feldman, Vahab Mirrokni, S. Muthukrishnan
BibTeX
Abstract
In light of the growing market of Ad Exchanges for the real-time sale of
advertising slots, publishers face new challenges in choosing between the
allocation of contract-based reservation ads and spot market ads. In this setting,
the publisher should take into account the tradeoff between short-term revenue from
an Ad Exchange and quality of allocating reservation ads. In this paper, we
formalize this combined optimization problem as a stochastic control problem and
derive an efficient policy for online ad allocation in settings with general joint
distribution over placement quality and exchange bids. We prove asymptotic
optimality of this policy in terms of any trade-off between quality of delivered
reservation ads and revenue from the exchange, and provide a rigorous bound for its
convergence rate to the optimal policy. We also give experimental results on data
derived from real publisher inventory, showing that our policy can achieve any
pareto-optimal point on the quality vs. revenue curve. Finally, we study a
parametric training-based algorithm in which instead of learning the dual variables
from a sample data (as is done in non-parametric training-based algorithms), we
learn the parameters of the distribution and construct those dual variables from
the learned parameter values. We compare parametric and non-parametric ways to
estimate from data both analytically and experimentally in the special case without
the ad exchange, and show that though both methods converge to the optimal policy
as the sample size grows, our parametric method converges faster, and thus performs
better on smaller samples.
