Predicting Pre-click Quality for Native Advertisements

By Ke Zhou, Miriam Redi, Andrew Haines, Mounia Lalmas. 2016

35 citations recorded by the source corpus.

Native advertising is a specific form of online advertising where ads replicate the look-and-feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experience 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 criteria drive user preferences in native advertising. We translate 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.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “, whether the ad annoys a user [27]; and nally the post-click experience, i.”