AdKDD 2024
Data Mining for Online Advertising 2024

Table of Contents

• Summary: There were 18 papers submitted for peer-review to this workshop. Out of these, 10 papers were accepted for this volume, 10 as regular papers and 0 as short papers.

• A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second
  Blaž Škrlj , Benjamin Ben-Shalom , Grega Gašperšič , Adi Schwartz , Ramzi Hoseisi , Naama Ziporin , Davorin Kopič , Andraž Tori
• Augmented Two-Stage Bandit Framework: Practical Approaches for Improved Online Ad Selection
  Seowon Han , Ryan Lakritz , Hanxiao Wu
• RankTower: A Synergistic Framework for Enhancing Two-Tower Pre-Ranking Model
  Yachen Yan , Liubo Li
• Leveraging Instrumental Variables in Online Advertising Auctions : Robust Click-Through-Rate Prediction
  Ryohei Emori , Shinya Suzumura , Shimizu Nobuyuki , Takahiro Hoshino
• SERP Interference Network and Its Applications in Search Advertising
  Purak Jain , Sandeep Appala
• AFA: Auto-tuning Filters for Ads
  Joobin Gharibshah , Mahmuda Rahman , Abraham Bagherjeiran
• Training Differentially Private Ad Prediction Models With Semi-Sensitive Features
  Lynn Chua , Qiliang Cui , Badih Ghazi , Charlie Harrison , Pritish Kamath , Walid Krichene , Ravi Kumar , Pasin Manurangsi , Nicolas Mayoraz , Hema Venkata Krishna Giri Narra , Steffen Rendle , Amer Sinha , Avinash Varadarajan , Chiyuan Zhang