research-article

Predicting Pre-click Quality for Native Advertisements

Authors:

Mounia LalmasAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 299 - 310

https://doi.org/10.1145/2872427.2883053

Published: 11 April 2016 Publication History

Abstract

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.

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Cited By

Yang GGhebreab EXu JQiu XYuan YSun W(2023)DeepPvisit: A Deep Survival Model for Notification ManagementCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587666(973-977)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587666
Yao ZKong DLu MBai XYang JXiong H(2023)Multi-View Multi-Task Campaign Embedding for Cold-Start Conversion Rate ForecastingIEEE Transactions on Big Data10.1109/TBDATA.2022.31621509:1(280-293)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3162150
Gharibshah ZZhu X(2021)User Response Prediction in Online AdvertisingACM Computing Surveys10.1145/344666254:3(1-43)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3446662
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Index Terms

Predicting Pre-click Quality for Native Advertisements
1. Information systems
  1. World Wide Web
    1. Online advertising

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cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://dl.acm.org/doi/10.1145/3543873.3587666
Yao ZKong DLu MBai XYang JXiong H(2023)Multi-View Multi-Task Campaign Embedding for Cold-Start Conversion Rate ForecastingIEEE Transactions on Big Data10.1109/TBDATA.2022.31621509:1(280-293)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3162150
Gharibshah ZZhu X(2021)User Response Prediction in Online AdvertisingACM Computing Surveys10.1145/344666254:3(1-43)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3446662
Foulds OAzzopardi LHalvey MScholer FThomas PElsweiler DJoho HKando NSmith C(2021)Investigating the Influence of Ads on User Search Performance, Behaviour, and Experience during Information SeekingProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446024(107-117)Online publication date: 14-Mar-2021
https://dl.acm.org/doi/10.1145/3406522.3446024
Tolomei GSilvestri F(2021)Generating Actionable Interpretations from Ensembles of Decision TreesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294532633:4(1540-1553)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TKDE.2019.2945326
Lalmas MBalog KSetty VLioma CLiu YZhang MBerberich K(2020)Personalising and Diversifying the Listening ExperienceProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3410464(3-3)Online publication date: 14-Sep-2020
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Hong LLalmas MGupta RLiu YShah MRajan STang JPrakash B(2020)Tutorial on Online User EngagementProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3406472(3551-3552)Online publication date: 23-Aug-2020
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de Oliveira LGoussevskaia OHung CCerny TShin DBechini A(2020)Sponsored content and user engagement dynamics on InstagramProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373956(1835-1842)Online publication date: 30-Mar-2020
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Zendel OFyodorov YRaiber FSilberstein NSomekh OTabaja Ad'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Leveraging User Email Actions to Improve Ad-Close PredictionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412093(2293-2296)Online publication date: 19-Oct-2020
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Silberstein NSomekh OKoren YAharon MPorat DShahar AWu TCaverlee JHu XLalmas MWang W(2020)Ad Close Mitigation for Improved User Experience in Native AdvertisementsProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371798(546-554)Online publication date: 20-Jan-2020
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