short-paper

Graph-Based Audience Expansion Model for Marketing Campaigns

Authors:

Md Mostafizur Rahman,

Daisuke Kikuta,

Toyotaro SuzumuraAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2970 - 2975

https://doi.org/10.1145/3626772.3661363

Published: 11 July 2024 Publication History

Abstract

Audience Expansion, a technique for identifying new audiences with similar behaviors to the original target or seed users. The major challenges include a heterogeneous user base, intricate marketing campaigns, constraints imposed by sparsity, and limited seed users, which lead to overfitting. In this context, we propose a novel solution named AudienceLinkNet, specifically designed to address the challenges associated with audience expansion in the context of Rakuten's diverse services and its clients. Our approach formulates the audience expansion problem as a graph problem and explores the combination of a Pre-trained Knowledge Graph Embedding Model and a Graph Convolutional Networks (GCNs). It emphasizes the structural retention properties of GCNs, enabling the model to overcome challenges related to cross-service data usage, sparsity and limited seed data. AudienceLinkNet simplifies the targeting process for small and large marketing campaigns and better utilizes demographics and behavioral attributes for targeting. Extensive experiments on our advertising platform, Rakuten AIris Target Prospecting, demonstrate the effectiveness of our audience expansion model. Additionally, we present the limitations of AudienceLinkNet.

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Cited By

Lin LChen XXia KWang SZhang DHe TSerra ESpezzano F(2024)Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience ExpansionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680062(4702-4709)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680062

Index Terms

Graph-Based Audience Expansion Model for Marketing Campaigns
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

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Published: 11 July 2024

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

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Cited By

Lin LChen XXia KWang SZhang DHe TSerra ESpezzano F(2024)Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience ExpansionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680062(4702-4709)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680062

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