Towards Global Optimization in Display Advertising by Integrating Multimedia Metrics with Real-Time Bidding

TL;DR

This paper proposes a novel framework that combines multimedia metrics with auction theory to optimize display advertising in real-time bidding, aiming to benefit all stakeholders involved.

Contribution

It introduces an integrated approach that considers multimedia effectiveness metrics within RTB, addressing the benefits of publishers, advertisers, and users.

Findings

Identified multimedia metrics influencing ad effectiveness

Integrated multimedia metrics into RTB framework

Preliminary results show potential for improved optimization

Abstract

Real-time bidding (RTB) has become a new norm in display advertising where a publisher uses auction models to sell online user's page view to advertisers. In RTB, the ad with the highest bid price will be displayed to the user. This ad displaying process is biased towards the publisher. In fact, the benefits of the advertiser and the user have been rarely discussed. Towards the global optimization, we argue that all stakeholders' benefits should be considered. To this end, we propose a novel computation framework where multimedia techniques and auction theory are integrated. This doctoral research mainly focus on 1) figuring out the multimedia metrics that affect the effectiveness of online advertising; 2) integrating the discovered metrics into the RTB framework. We have presented some preliminary results and discussed the future directions.