research-article

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Authors:

Alfonso Lobos Ruiz,

Aaron FloresAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2689 - 2697

https://doi.org/10.1145/3292500.3330783

Published: 25 July 2019 Publication History

Abstract

Conversion prediction plays an important role in online advertis- ing since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click pre- diction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representa- tions, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two types of conversions, and the weighted AUC across all conversion types is also improved by 0.50%.

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Cited By

Yang EShen LWang ZGuo GChen XWang XTao DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Representation surgery for multi-task model mergingProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694395(56332-56356)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694395
Song DYang EGuo GShen LJiang LWang X(2024)Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/365131218:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3651312
Li KChen XLeng LXu JSun JRezaei B(2024)Privacy Preserving Conversion Modeling in Data Clean RoomProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688054(819-822)Online publication date: 8-Oct-2024
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Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

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Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Published: 25 July 2019

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August 4 - 8, 2019

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

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

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Cited By

Yang EShen LWang ZGuo GChen XWang XTao DSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Representation surgery for multi-task model mergingProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694395(56332-56356)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694395
Song DYang EGuo GShen LJiang LWang X(2024)Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/365131218:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3651312
Li KChen XLeng LXu JSun JRezaei B(2024)Privacy Preserving Conversion Modeling in Data Clean RoomProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688054(819-822)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688054
Pan JXue WWang XYu HLiu XQuan SQiu XLiu DXiao LJiang JBaeza-Yates RBonchi F(2024)Ads Recommendation in a Collapsed and Entangled WorldProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671607(5566-5577)Online publication date: 25-Aug-2024
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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
Wu CLian DGe YZhou MChen ETao D(2024)Boosting Factorization Machines via Saliency-Guided MixupIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335491046:6(4443-4459)Online publication date: Jun-2024
https://doi.org/10.1109/TPAMI.2024.3354910
Ouyang WZhang XGuo CRen SSui YZhang KLuo JChen YXu DLiu XDu YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single ModelProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614697(4773-4779)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614697
Pavlovski MRavindran SGligorijevic DAgrawal SStojkovic ISegura-Nunez NGligorijevic JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Extreme Multi-Label Classification for Ad Targeting using Factorization MachinesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599822(4705-4716)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599822
Wang YSun PZhang MJia QLi JMa SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Unbiased Delayed Feedback Label Correction for Conversion Rate PredictionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599536(2456-2466)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599536
Ouyang WDong RZhang XGuo CLuo JLiu XDu YChen HDuh WHuang HKato MMothe JPoblete B(2023)Contrastive Learning for Conversion Rate PredictionProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591968(1909-1913)Online publication date: 19-Jul-2023
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