MM2RTB: bringing multimedia metrics to real-time bidding

Chen, Xiang, Chen, Bowei and Kankanhalli, Mohan (2017) MM2RTB: bringing multimedia metrics to real-time bidding. In: KDD Workshop, 13 - 17 August, 2017, Halifax, Nova Scotia, Canada.

1708.00255.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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.

Keywords:Display advertising, Real-time bidding, Multimedia metrics
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G920 Others in Computing Sciences
G Mathematical and Computer Sciences > G200 Operational Research
N Business and Administrative studies > N500 Marketing
G Mathematical and Computer Sciences > G450 Multi-media Computing Science
G Mathematical and Computer Sciences > G500 Information Systems
Divisions:College of Science > School of Computer Science
ID Code:31094
Deposited On:12 Mar 2018 11:55

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