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short-paper

GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction

Authors:

Jingyuan Zhang,

Ping LiAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2166 - 2171

https://doi.org/10.1145/3404835.3463116

Published: 11 July 2021 Publication History

Abstract

Deep neural network (DNN) models have been widely used for click-through rate (CTR) prediction in online advertising. The training framework typically consists of embedding layers and multi-layer perceptions (MLP). At Baidu Search Ads (a.k.a. Phoenix Nest), the new generation of CTR training platform has become PaddleBox, a GPU-based parameter server system. In this paper, we present Baidu's recently updated CTR training framework, called Gating-enhanced Multi-task Neural Networks (GemNN). In particular, we develop a neural network based multi-task learning model to predict CTR in a coarse-to-fine manner, which gradually reduces ad candidates and allows parameter sharing from upstream tasks to downstream tasks to improve the training efficiency. Also, we introduce a gating mechanism between embedding layers and MLP to learn feature interactions and control the information flow fed to MLP layers. We have launched our solution in Baidu PaddleBox platform and observed considerable improvements in both offline and online evaluations. It is now part of the current production~system.

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Cited By

Yuan RMeng SDou RWang X(2024)Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge ComputingTsinghua Science and Technology10.26599/TST.2022.901005429:1(86-98)Online publication date: Feb-2024
https://doi.org/10.26599/TST.2022.9010054
Sang LLi HZhang YZhang YYang Y(2024)AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate PredictionACM Transactions on Information Systems10.1145/368178543:1(1-31)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681785
Li HSang LZhang YZhang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SimCEN: Simple Contrast-enhanced Network for CTR PredictionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681203(2311-2320)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681203
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Index Terms

GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction
1. Information systems
  1. Information systems applications
    1. Computational advertising

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

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Cited By

Yuan RMeng SDou RWang X(2024)Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge ComputingTsinghua Science and Technology10.26599/TST.2022.901005429:1(86-98)Online publication date: Feb-2024
https://doi.org/10.26599/TST.2022.9010054
Sang LLi HZhang YZhang YYang Y(2024)AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate PredictionACM Transactions on Information Systems10.1145/368178543:1(1-31)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3681785
Li HSang LZhang YZhang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SimCEN: Simple Contrast-enhanced Network for CTR PredictionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681203(2311-2320)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681203
Lin JLi QXie GGuan ZJiang YXu TZhang ZZhao PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680615(7581-7590)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680615
Zhao WTan SLi PHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GUITAR: Gradient Pruning toward Fast Neural RankingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657728(163-173)Online publication date: 10-Jul-2024
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Huang TOu XYang HHu SGeng JXu ZYang Z(2024)Pull together: Option-weighting-enhanced mixture-of-experts knowledge tracingExpert Systems with Applications10.1016/j.eswa.2024.123419248(123419)Online publication date: Aug-2024
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Tao XHa MMa QCheng HLin WGuo XCheng LHan B(2023)Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task LearningProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608772(151-160)Online publication date: 14-Sep-2023
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Wang FGu HLi DLu TZhang PGu NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Towards Deeper, Lighter and Interpretable Cross Network for CTR PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615089(2523-2533)Online publication date: 21-Oct-2023
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Doan KTan SZhao WLi PChen HDuh WHuang HKato MMothe JPoblete B(2023)Asymmetric Hashing for Fast Ranking via Neural Network MeasuresProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591640(697-707)Online publication date: 19-Jul-2023
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Ji SYang B(2023)Sequence Separation-Based Modeling of Denoised Implicit Feedback Behavior2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI58017.2023.00056(283-288)Online publication date: Aug-2023
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