research-article

A Request-level Guaranteed Delivery Advertising Planning: Forecasting and Allocation

Authors:

Lan Xu,

Wei Xu,

Yiguo YangAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2980 - 2988

https://doi.org/10.1145/3394486.3403348

Published: 20 August 2020 Publication History

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Abstract

The guaranteed delivery model is widely used in online advertising. The publisher sells impressions in advance by promising to serve each advertiser an agreed-upon number of target impressions that satisfy specific attribute requirements over a fixed time period. Previous efforts usually model the service as a crowd-level or user-level supply allocation problem and focus on searching optimal allocation for online serving, assuming that forecasts of supply are available and contracts are already signed. Existing techniques are not sufficient to meet the needs of today's industry trends: 1) advertisers pursue more precise targeting, which requires not only user-level attributes but also request-level attributes; 2) users prefer more friendly ad serving, which imposes more diverse serving constraints; 3) the bottleneck of the publisher's revenue growth lies in not only the ad serving, but also the forecast accuracy and sales strategy. These issues are non-trivial to address, since the scale of the request-level model is orders of magnitude larger than that of the crowd-level or user-level models. Facing the challenges, we present a holistic design of a request-level guaranteed delivery advertising planning system with careful optimization for all three critical components including impression forecasting, selling and serving. Our system has been deployed in the Tencent online guaranteed delivery advertising system serving billion level users for nearly one year. Evaluations on large-scale real data and the performance of the deployed system both demonstrate that our design can significantly increase the request-level impression forecast accuracy and delivery rate.

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The guarantee delivery model is widely used in online advertising. The publisher sells impressions in advance by promising each advertiser an agreed-upon number of impressions that satisfy specific attribute requirements. Existing techniques are not enough to meet the industry needs: 1) advertisers pursue more precise targeting, requiring not only user-level but also request-level attributes; 2) users prefer more friendly ad serving, which imposes more serving constraints; 3) revenue growth stems from not only ad serving, but also impression forecasting and selling. These issues are non-trivial to address, since the scale of the request-level model is orders of magnitude larger than the user-level one. We present a request-level guaranteed delivery advertising planning system with careful optimization for three critical components including impression forecasting, selling and serving. Evaluations on large-scale real data demonstrate that our design significantly increase the forecast accuracy and play rate.

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Chen XLyu JWang YZhou Y(2024)Network Revenue Management With Demand Learning and Fair Resource-Consumption BalancingProduction and Operations Management10.1177/1059147823122517633:2(494-511)Online publication date: 6-Mar-2024
https://doi.org/10.1177/10591478231225176
He XMao WXu ZGu YHuang YZu ZWang LZhao MZou MBaeza-Yates RBonchi F(2024)An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear ConstraintsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671811(1040-1049)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671811
Li YHuang YMao WYe FHe XZu ZCai SBaeza-Yates RBonchi F(2024)Bi-Objective Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671752(1691-1700)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671752
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Index Terms

A Request-level Guaranteed Delivery Advertising Planning: Forecasting and Allocation
1. Information systems
  1. Information systems applications
    1. Computational advertising
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Models of learning

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Published In

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

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Research-article

Funding Sources

China National Funds for Distinguished Young Scientists
National Key R&D Program of China
National Natural Science Foundation of China
the Fundamental Research Funds for the Central Universities

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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https://doi.org/10.1177/10591478231225176
He XMao WXu ZGu YHuang YZu ZWang LZhao MZou MBaeza-Yates RBonchi F(2024)An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear ConstraintsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671811(1040-1049)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671811
Li YHuang YMao WYe FHe XZu ZCai SBaeza-Yates RBonchi F(2024)Bi-Objective Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671752(1691-1700)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671752
Yang ZGhosh MSaha AXu DShmakov KLee K(2024)A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825167(1151-1160)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825167
Dai WLiu MChu CLiu M(2024)Robust optimization for spread quality and shortfall in guaranteed targeted display advertising planningComputers & Operations Research10.1016/j.cor.2023.106421161(106421)Online publication date: Jan-2024
https://doi.org/10.1016/j.cor.2023.106421
Zhang QYe WChen ZHu HChen EYang YKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Nearly optimal competitive ratio for online allocation problems with two-sided resource constraints and finite requestsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620163(41786-41818)Online publication date: 23-Jul-2023
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Wang XGuo YMa XHuang DXu LTan HZhou HLi XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CLOCK: Online Temporal Hierarchical Framework for Multi-scale Multi-granularity Forecasting of User ImpressionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614810(2544-2553)Online publication date: 21-Oct-2023
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Mao WLiu CHuang YZu ZHarshvardhan MWang LZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery AdvertisingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599332(1677-1686)Online publication date: 6-Aug-2023
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Ali APinciroli RYan FSmirni E(2022)Optimizing inference serving on serverless platformsProceedings of the VLDB Endowment10.14778/3547305.354731315:10(2071-2084)Online publication date: 7-Sep-2022
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