2017/08/01 by Xiang Chen, Chen, Xiang, Bowei Chen +3
Computer Science · Social Sciences · #Advanced Image and Video Retrieval Techniques #Bidding #Business #Computer science #FOS: Computer and information sciences #Marketing #Multimedia #Multimedia (cs.MM) #Multimedia Communication and Technology #Video Analysis and Summarization #cs.MM
paper · pdf · doi:10.48550/arxiv.1708.00255
In Proceedings of AdKDD and TargetAd, Halifax, NS, Canada, August, 14, 2017, 6 pages
arxiv created 2017/08/01 · openalex publication_date 2017/08/01 · arxiv updated 2017/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.