short-paper

ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction

Authors:

Haoyuan HuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1786 - 1790

https://doi.org/10.1145/3539618.3591944

Published: 18 July 2023 Publication History

Abstract

Large-scale commercial platforms usually involve numerous business scenarios for diverse business strategies. To provide click-through rate (CTR) predictions for multiple scenarios simultaneously, existing promising multi-scenario models explicitly construct scenario-specific networks by manually grouping scenarios based on particular business strategies. Nonetheless, this pre-defined data partitioning process heavily relies on prior knowledge, and it may neglect the underlying data distribution of each scenario, hence limiting the model's representation capability. Regarding the above issues, we propose Adaptive Distribution Learning (ADL): an end-to-end optimization distribution framework which is composed of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Our results on both public and large-scale industrial datasets show the effectiveness and efficiency of ADL: the model yields impressive prediction accuracy with more than 50% reduction in time cost during the training phase when compared to other methods.

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Cited By

Zhang MTang YHu JZhang YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario ContextProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657803(1557-1566)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657803
Kong MHou MZhao SLiu FSu RChen Y(2024)DADINExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122880243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122880
Li YHou LLi DLi J(2023)HKGCLExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120963233:COnline publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120963

Index Terms

ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Zhang MTang YHu JZhang YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario ContextProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657803(1557-1566)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657803
Kong MHou MZhao SLiu FSu RChen Y(2024)DADINExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122880243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122880
Li YHou LLi DLi J(2023)HKGCLExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120963233:COnline publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120963

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