A novel auction system for selecting advertisements in Real-Time bidding
This work addresses the need for more balanced and efficient ad selection in RTB networks, though it appears incremental by building on existing auction frameworks.
The paper tackles the problem of selecting advertisements in Real-Time Bidding by proposing an alternative auction system that incorporates multiple factors beyond price, such as advertiser benefit and conversion probability, and demonstrates its performance through experiments comparing it to the Generalized Second-Price method.
Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. This system works like a global auction where advertisers bid to display their impressions in the publishers' ad slots. The most popular system to select which advertiser wins each auction is the Generalized second-price auction in which the advertiser that offers the most wins the bet and is charged with the price of the second largest bet. In this paper, we propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system. The factors that we consider are, among others, the benefit that can be given to each advertiser, the probability of conversion from the advertisement, the probability that the visit is fraudulent, how balanced are the networks participating in RTB and if the advertisers are not paying over the market price. In addition, we propose a methodology based on genetic algorithms to optimize the selection of each advertiser. We also conducted some experiments to compare the performance of the proposed model with the famous Generalized Second-Price method. We think that this new approach, which considers more relevant aspects besides the price, offers greater benefits for RTB networks in the medium and long-term.