5 research outputs found
Optimal delivery in display advertising
In display advertising, a publisher targets a specific audience by displaying ads on content web pages. Because the publisher has little control over the supply of display opportunities, the actual supply of ads that it can sell is stochastic. We consider the problem of optimal ad delivery, where an advertiser requests a certain number of impressions to be displayed by the publisher over a certain time horizon. Time is divided into periods, and in the beginning of each period the publisher chooses a fraction of the still unrealized supply to allocate towards fulfilling the advertiser's demand. The goal is to be able to fulfill the demand at the end of the horizon with minimal costs incurred from penalties associated with shortage or over-delivery of ads. We describe optimal policies that are both simple in structure and easy to implement for several variations of this problem
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting
Optimal delivery in display advertising
In display advertising, a publisher targets a specific audience by displaying ads on content web pages. Because the publisher has little control over the supply of display opportunities, the actual supply of ads that it can sell is stochastic. We consider the problem of optimal ad delivery, where an advertiser requests a certain number of impressions to be displayed by the publisher over a certain time horizon. Time is divided into periods, and in the beginning of each period the publisher chooses a fraction of the still unrealized supply to allocate towards fulfilling the advertiser's demand. The goal is to be able to fulfill the demand at the end of the horizon with minimal costs incurred from penalties associated with shortage or over-delivery of ads. We describe optimal policies that are both simple in structure and easy to implement for several variations of this problem
Expressive Banner Ad Auctions and Model-Based Online Optimization for Clearing
We present the design of a banner advertising auction which is considerably more expressive than current designs. We describe a general model of expressive ad contracts/bidding and an allocation model that can be executed in real time through the assignment of fractions of relevant ad channels to specific advertiser contracts. The uncertainty in channel supply and demand is addressed by the formulation of a stochastic combinatorial optimization problem for channel allocation that is rerun periodically. We solve this in two different ways: fast deterministic optimization with respect to expectations; and a novel online sample-based stochastic optimization method— that can be applied to continuous decision spaces—which exploits the deterministic optimization as a black box. Experiments demonstrate the importance of expressive bidding and the value of stochastic optimization.