A novel auction system for selecting advertisements in Real-Time bidding

TL;DR

This paper introduces a new auction system for Real-Time Bidding that incorporates multiple factors beyond price, such as conversion probability and fraud risk, optimized using genetic algorithms, aiming for better long-term benefits.

Contribution

It proposes an innovative auction model that considers multiple relevant factors and uses genetic algorithms for optimization, improving upon the traditional GSP method.

Findings

The new model outperforms the GSP method in experiments.

Incorporating multiple factors leads to more balanced and beneficial ad selections.

The approach enhances long-term performance of RTB networks.

Abstract

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.