5 research outputs found
Truthful Auctions for Automated Bidding in Online Advertising
Automated bidding, an emerging intelligent decision making paradigm powered
by machine learning, has become popular in online advertising. Advertisers in
automated bidding evaluate the cumulative utilities and have private financial
constraints over multiple ad auctions in a long-term period. Based on these
distinct features, we consider a new ad auction model for automated bidding:
the values of advertisers are public while the financial constraints, such as
budget and return on investment (ROI) rate, are private types. We derive the
truthfulness conditions with respect to private constraints for this
multi-dimensional setting, and demonstrate any feasible allocation rule could
be equivalently reduced to a series of non-decreasing functions on budget.
However, the resulted allocation mapped from these non-decreasing functions
generally follows an irregular shape, making it difficult to obtain a
closed-form expression for the auction objective. To overcome this design
difficulty, we propose a family of truthful automated bidding auction with
personalized rank scores, similar to the Generalized Second-Price (GSP)
auction. The intuition behind our design is to leverage personalized rank
scores as the criteria to allocate items, and compute a critical ROI to
transform the constraints on budget to the same dimension as ROI. The
experimental results demonstrate that the proposed auction mechanism
outperforms the widely used ad auctions, such as first-price auction and
second-price auction, in various automated bidding environments
Machine learning for targeted display advertising: Transfer learning in action
This paper presents a detailed discussion of problem formulation and
data representation issues in the design, deployment, and operation of a
massive-scale machine learning system for targeted display advertising.
Notably, the machine learning system itself is deployed and has been in
continual use for years, for thousands of advertising campaigns (in
contrast to simply having the models from the system be deployed). In
this application, acquiring sufficient data for training from the ideal
sampling distribution is prohibitively expensive. Instead, data are
drawn from surrogate domains and learning tasks, and then transferred
to the target task. We present the design of this multistage transfer
learning system, highlighting the problem formulation aspects. We then
present a detailed experimental evaluation, showing that the different
transfer stages indeed each add value. We next present production
results across a variety of advertising clients from a variety of
industries, illustrating the performance of the system in use. We close
the paper with a collection of lessons learned from the work over half a
decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie
User Interaction with Online Advertisements: Temporal Modeling and Optimization of Ads Placement
We consider an online advertisement system and focus on the impact of user interaction and response to targeted advertising campaigns. We analytically model the system dynamics accounting for the user behavior and devise strategies to maximize a relevant metric called click-through-intensity (CTI), defined as the number of clicks per time unit. With respect to the traditional click-through-rate (CTR) metric, CTI better captures the success of advertisements for services that the users may access several times, making multiple purchases or subscriptions. Examples include advertising of on-line games or airplane tickets. The model we develop is validated through traces of real advertising systems and allows us to optimize CTI under different scenarios depending on the nature of ad delivery and of the information available at the system. Experimental results show that our approach can increase the revenue of an ad campaign, even when user’s behavior can only be estimated