Predicting pre-click quality for native advertisements

Zhou, Ke; Redi, Miriam; Haines, Andrew; Lalmas, Mounia

Abstract

Native advertising is a speciffic form of online advertising where ads replicate the look-And-feel of their serving plat-form. In such context, providing a good user experience with the served ads is crucial to ensure long-Term user en-gagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experi-ence perspective. We design a learning framework to predict the pre-click quality of native ads. More specifically, we look at detecting offensive native ads, showing that, to quantify ad quality, ad offensive user feedback rates are more reliable than the commonly used click-Through rate metrics. We then conduct a crowd-sourcing study to identify which cri-teria drive user preferences in native advertising. We trans-late these criteria into a set of ad quality features that we extract from the ad text, image and advertiser, and then use them to train a model able to identify offensive ads. We show that our model is very effective in detecting offensive ads, and provide in-depth insights on how different features affect ad quality. Finally, we deploy a preliminary version of such model and show its effectiveness in the reduction of the offensive ad feedback rate.

Citation

Zhou, K., Redi, M., Haines, A., & Lalmas, M. (2016, April). Predicting pre-click quality for native advertisements. Presented at 25th International World Wide Web Conference, WWW 2016, Montréal Québec Canada

• Presentation Conference Type: Edited Proceedings
• Conference Name: 25th International World Wide Web Conference, WWW 2016
• Start Date: Apr 11, 2016
• End Date: Apr 15, 2016
• Acceptance Date: Dec 15, 2015
• Online Publication Date: Apr 11, 2016
• Publication Date: Apr 11, 2016
• Deposit Date: Sep 18, 2017
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 2016-April
• Pages: 299-310
• Book Title: WWW '16 Proceedings of the 25th International Conference on World Wide Web
• ISBN: 9781450341431
• DOI: https://doi.org/10.1145/2872427.2883053
• Public URL: https://nottingham-repository.worktribe.com/output/1125885
• Publisher URL: https://dl.acm.org/citation.cfm?doid=2872427.2883053