mlsanityData to 7 Oct 2026

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

arXiv 1610.03013 ↗First posted 7 Oct 2016cs.GTcited by 42

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Abstract (arXiv)

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user's visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 10; 2023: 3; 2024: 8; 2025: 8; 2026: 6.

42 papers.

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  1. 2021When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?cited by 63
  2. 2021A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertisingcited by 22
  3. 2021Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modelingcited by 20
  4. 2021User Response Prediction in Online Advertisingcited by 15
  5. 2025Agent Exchange: Shaping the Future of AI Agent Economicscited by 15
  6. 2024Ads Recommendation in a Collapsed and Entangled Worldcited by 13
  7. 2025RTBAgent: A LLM-based Agent System for Real-Time Biddingcited by 13
  8. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
  9. 2022On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspectivecited by 8
  10. 2024Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbotscited by 7
  11. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  12. 2025Learning to Bid in Non-Stationary Repeated First-Price Auctionscited by 5
30 more
  1. 2025See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Predictioncited by 5
  2. 2025Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Alignercited by 5
  3. 2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioningcited by 4
  4. 2022Real-time Bidding Strategy in Display Advertising: An Empirical Analysiscited by 4
  5. 2021Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertisingcited by 3
  6. 2024MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertisingcited by 3
  7. 2024Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)cited by 3
  8. 2021Scaling TensorFlow to 300 million predictions per secondcited by 2
  9. 2022A Real-World Implementation of Unbiased Lift-based Bidding Systemcited by 2
  10. 2025Expert-Guided Diffusion Planner for Auto-Biddingcited by 2
  11. 2021Bid Optimization using Maximum Entropy Reinforcement Learningcited by 1
  12. 2022Exploration with Model Uncertainty at Extreme Scale in Real-Time Biddingcited by 1
  13. 2022Advertising Media and Target Audience Optimization via High-dimensional Banditscited by 1
  14. 2022Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithmcited by 1
  15. 2022Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Fieldcited by 1
  16. 2024DCRMTA: Deep Causal Representation Learning for Multi-Touch Attributioncited by 1
  17. 2024AIGB: Generative Auto-bidding via Conditional Diffusion Modelingcited by 1
  18. 2025Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lenscited by 1
  19. 2026PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspectivecited by 1
  20. 2026Error-Aware Reverse Auction Mechanism for Large Language Model Routingcited by 1
  21. 2022Search and Score-based Waterfall Auction Optimizationcited by 0
  22. 2022Functional Optimization Reinforcement Learning for Real-Time Biddingcited by 0
  23. 2023Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Modelscited by 0
  24. 2023Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUFcited by 0
  25. 2024Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Biddingcited by 0
  26. 2025Multi-agent Adaptive Mechanism Designcited by 0
  27. 2026Statistical Learning from Attribution Setscited by 0
  28. 2026A Lightweight MPC Bidding Framework for Brand Auction Adscited by 0
  29. 2026AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimizationcited by 0
  30. 2026OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarioscited by 0