Title: MM2RTB: Bringing Multimedia Metrics to Real-Time Bidding,
Abstract: In display advertising, users' online ad experiences are important for the
advertising effectiveness. However, users have not been well accommodated in
real-time bidding (RTB). This further influences their site visits and
perception of the displayed banner ads. In this paper, we propose a novel
computational framework which brings multimedia metrics, like the contextual
relevance, the visual saliency and the ad memorability into RTB to improve the
users' ad experiences as well as maintain the benefits of the publisher and the
advertiser. We aim at developing a vigorous ecosystem by optimizing the
trade-offs among all stakeholders. The framework considers the scenario of a
webpage with multiple ad slots. Our experimental results show that the benefits
of the advertiser and the user can be significantly improved if the publisher
would slightly sacrifice his short-term revenue. The improved benefits will
increase the advertising requests (demand) and the site visits (supply), which
can further boost the publisher's revenue in the long run.