research-article

Bid landscape forecasting in online ad exchange marketplace

Authors:

Ying Cui,

Ruofei Zhang,

Wei Li,

Jianchang MaoAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 265 - 273

https://doi.org/10.1145/2020408.2020454

Published: 21 August 2011 Publication History

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Abstract

Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One important business model in online display advertising is Ad Exchange marketplace, also called non-guaranteed delivery (NGD), in which advertisers buy targeted page views and audiences on a spot market through real-time auction. In this paper, we describe a bid landscape forecasting system in NGD marketplace for any advertiser campaign specified by a variety of targeting attributes. In the system, the impressions that satisfy the campaign targeting attributes are partitioned into multiple mutually exclusive samples. Each sample is one unique combination of quantified attribute values. We develop a divide-and-conquer approach that breaks down the campaign-level forecasting problem. First, utilizing a novel star-tree data structure, we forecast the bid for each sample using non-linear regression by gradient boosting decision trees. Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions. The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.

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Cited By

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Luo XChen YZhuo SLu JChen ZLi LTian JYe XTang Y(2024)Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price LandscapesBig Data and Cognitive Computing10.3390/bdcc80500468:5(46)Online publication date: 28-Apr-2024
https://doi.org/10.3390/bdcc8050046
Wang ZZheng ZKang YHuang JAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Cost-Effective Active Learning for Bid Exploration in Online AdvertisingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635839(788-796)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635839
Huang JZheng ZKang YWang ZAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)From Second to First: Mixed Censored Multi-Task Learning for Winning Price PredictionProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635838(295-303)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635838
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Index Terms

Bid landscape forecasting in online ad exchange marketplace
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web

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KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Luo XChen YZhuo SLu JChen ZLi LTian JYe XTang Y(2024)Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price LandscapesBig Data and Cognitive Computing10.3390/bdcc80500468:5(46)Online publication date: 28-Apr-2024
https://doi.org/10.3390/bdcc8050046
Wang ZZheng ZKang YHuang JAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Cost-Effective Active Learning for Bid Exploration in Online AdvertisingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635839(788-796)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635839
Huang JZheng ZKang YWang ZAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)From Second to First: Mixed Censored Multi-Task Learning for Winning Price PredictionProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635838(295-303)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635838
Stram RAbboud RShtoff ASomekh ORaviv AKoren YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Mystique: A Budget Pacing System for Performance Optimization in Online AdvertisingCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648342(433-442)Online publication date: 13-May-2024
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Yakovleva DTelnov SMakarov IFilchenkov A(2024)Bid Landscape Forecasting and Cold Start Problem With TransformersIEEE Access10.1109/ACCESS.2024.336049312(19117-19127)Online publication date: 2024
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Jha AJain HSharma PSharma YTiwari K(2024)Optimizing Real-Time Bidding Strategies: An Experimental Analysis of Reinforcement Learning and Machine Learning TechniquesProcedia Computer Science10.1016/j.procs.2024.04.191235(2017-2026)Online publication date: 2024
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Ou WChen BDai XZhang WLiu WTang RYu Y(2023)A Survey on Bid Optimization in Real-Time Bidding Display AdvertisingACM Transactions on Knowledge Discovery from Data10.1145/362860318:3(1-31)Online publication date: 9-Dec-2023
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Ou WChen BYang YDai XLiu WZhang WTang RYu YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Deep Landscape Forecasting in Multi-Slot Real-Time BiddingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599799(4685-4695)Online publication date: 6-Aug-2023
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Kalra AWang CBorcea CChen Y(2023)Reserve Price optimization in First-Price Auctions via Multi-Task Learning2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00029(200-209)Online publication date: 1-Dec-2023
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Wang TYang HLiu YYu HSong H(2023)A multimodal approach for improving market price estimation in online advertisingKnowledge-Based Systems10.1016/j.knosys.2023.110392266:COnline publication date: 22-Apr-2023
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Deep Landscape Forecasting for Real-time Bidding Advertising

Scalable Bid Landscape Forecasting in Real-Time Bidding

A Bayesian Multi-Armed Bandit Algorithm for Bid Shading in Online Display Advertising

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