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 and Andraž Tori

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AFA: Auto-tuning Filters for Ads

Joobin Gharibshah, Mahmuda Rahman, Abraham Bagherjeiran

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Augmented Two-Stage Bandit Framework: Practical Approaches for Improved Online Ad Selection

Seowon Han, Ryan Lakritz and Hanxiao Wu

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Cost-Control in Display Advertising: Theory vs Practice

Anoop R Katti, Rui C. Gonçalves and Rinchin Iakovlev

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Leveraging Instrumental Variables in Online Advertising Auctions : Robust Click-Through-Rate Prediction

Ryohei Emori, Shinya Suzumura, Shimizu Nobuyuki and Takahiro Hoshino

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RankTower: A Synergistic Framework for Enhancing Two-Tower Pre-Ranking Model

Yachen Yan and Liubo Li

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SERP Interference Network and its Applications in Search Advertising

Purak Jain and Sandeep Appala

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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 and Chiyuan Zhang

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Trigger Relevancy and Diversity Inefficiency with Dual-Phase Synergistic Attention in Shopee Recommendation Ads System

Sheng Li and Zhiwei Chen

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Criteo: AdTech in 2024 Challenges and Perspectives

Romain Lerallut

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Supercharging Jobs Marketplace: Optimizing Hiring Outcomes, Unified Jobs Marketplace, Big Auctions and Beyond

Liangjie Hong

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Multi-Task Combinatorial Bandits for Budget Allocation

Lin Ge, Yang Xu, Jianing Chu, David Cramer, Fuhong Li, Kelly Paulson and Rui Song

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