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Optimized Cost per Click in Taobao Display Advertising

Authors:

Kun GaiAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2191 - 2200

https://doi.org/10.1145/3097983.3098134

Published: 13 August 2017 Publication History

Abstract

Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.

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Yang YChen BZhu CZhu MDai XGuo HZhang MDong ZTang R(2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688136
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Index Terms

Optimized Cost per Click in Taobao Display Advertising
1. Information systems
  1. Information systems applications
    1. Computational advertising
  2. World Wide Web
    1. Online advertising
      1. Display advertising

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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: 13 August 2017

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

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

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Cited By

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https://doi.org/10.3233/WEB-230346
Yang YChen BZhu CZhu MDai XGuo HZhang MDong ZTang R(2024)AIE: Auction Information Enhanced Framework for CTR Prediction in Online AdvertisingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688136(633-642)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688136
Chen SLi SChen YYang D(2024)Entity-Aware Collections Ranking: A Joint Scoring ApproachProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688038(784-786)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688038
Zhang KYuan ZXiong HBaeza-Yates RBonchi F(2024)Optimized Cost Per Click in Online Advertising: A Theoretical AnalysisProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671767(4232-4243)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671767
Su YXiang MChen YLi YQin TZhang HLi YLiu XBaeza-Yates RBonchi F(2024)Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671540(5731-5740)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671540
Yang SYang HZou ZXu LYuan SZeng YBaeza-Yates RBonchi F(2024)Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671529(6117-6126)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671529
Sheng XYang FGong LWang BChan ZZhang YCheng YZhu YGe TZhu HJiang YXu JZheng BSerra ESpezzano F(2024)Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and InsightsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680068(4858-4865)Online publication date: 21-Oct-2024
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Wu XZhou HShi YYao WHuang XLiu NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645494(3566-3575)Online publication date: 13-May-2024
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Wang HTang BLiu CMao SZhou JDai ZSun YXie QWang XWang D(2024)HiBid: A Cross-Channel Constrained Bidding System With Budget Allocation by Hierarchical Offline Deep Reinforcement LearningIEEE Transactions on Computers10.1109/TC.2023.334311173:3(815-828)Online publication date: Mar-2024
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