Predicting Pre-click Quality for Native Advertisements

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.

[1]  W. Chu.  Studying Aesthetics in Photographic Images Using a Computational Approach , 2013 .

[2]  Zhou Wang,et al.  No-reference perceptual quality assessment of JPEG compressed images , 2002, Proceedings. International Conference on Image Processing.

[3]  Yang Zhou,et al.  Multimedia features for click prediction of new ads in display advertising , 2012, KDD.

[4]  Hema Raghavan,et al.  Improving ad relevance in sponsored search , 2010, WSDM '10.

[5]  Jan Panero Benway,et al.  Banner Blindness: The Irony of Attention Grabbing on the World Wide Web , 1998 .

[6]  Henriette Cramer,et al.  People's Perceptions of Personalized Ads , 2015, WWW.

[7]  Vasudeva Varma,et al.  Computational Advertising: Techniques for Targeting Relevant Ads , 2014 .

[8]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[9]  Rainer Lienhart,et al.  A survey on visual adult image recognition , 2012, Multimedia Tools and Applications.

[10]  Henriette Cramer,et al.  Effects of Ad Quality & Content-Relevance on Perceived Content Quality , 2015, CHI.

[11]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[12]  Liming Chen,et al.  WebGuard: Web based adult content detection and filtering system , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[13]  Jean-Marc Dewaele,et al.  Formality of Language: definition, measurement and behavioral determinants , 1999 .

[14]  Miriam Redi,et al.  The beauty of capturing faces: Rating the quality of digital portraits , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[15]  C. Yoo,et al.  Processing of animation in online banner advertising: The roles of cognitive and emotional responses , 2005 .

[16]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[17]  Chong Wang,et al.  Viewability Prediction for Online Display Ads , 2015, CIKM.

[18]  Jing Zhou,et al.  Hate Speech Detection with Comment Embeddings , 2015, WWW.

[19]  Elaine Toms,et al.  The development and evaluation of a survey to measure user engagement , 2010, J. Assoc. Inf. Sci. Technol..

[20]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[21]  Tie-Yan Liu,et al.  Psychological advertising: exploring user psychology for click prediction in sponsored search , 2013, KDD.

[22]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Torsten Suel,et al.  Modeling and predicting user behavior in sponsored search , 2009, KDD.

[24]  Falk Scholer,et al.  The effect of threshold priming and need for cognition on relevance calibration and assessment , 2013, SIGIR.

[25]  Jian Hu,et al.  Optimizing search engine revenue in sponsored search , 2009, SIGIR.

[26]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[27]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[28]  Wentong Li,et al.  Estimating conversion rate in display advertising from past erformance data , 2012, KDD.

[29]  Edward M. Riseman,et al.  TextFinder: An Automatic System to Detect and Recognize Text In Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Avi Goldfarb,et al.  Online Display Advertising: Targeting and Obtrusiveness , 2011, Mark. Sci..

[31]  R. Preston McAfee,et al.  The cost of annoying ads , 2013, WWW '13.

[32]  Mark Sanderson,et al.  Do user preferences and evaluation measures line up? , 2010, SIGIR.

[33]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Yiming Yang,et al.  Support vector machines classification with a very large-scale taxonomy , 2005, SKDD.

[35]  Jiawei Han,et al.  Survey on web spam detection: principles and algorithms , 2012, SKDD.

[36]  Allan Hanbury,et al.  Affective image classification using features inspired by psychology and art theory , 2010, ACM Multimedia.

[37]  R. Koenker.  Quantile Regression: Name Index , 2005 .

[38]  Yong Li,et al.  Image Feature Learning for Cold Start Problem in Display Advertising , 2015, IJCAI.

[39]  R. Koenker.  Quantile Regression: Fundamentals of Quantile Regression , 2005 .

[40]  Fabrizio Silvestri,et al.  Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users , 2015, KDD.