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