tutorial

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Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges

Authors:

Erasmo Purificato,

Ludovico Boratto,

Ernesto William De LucaAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 5216 - 5219

https://doi.org/10.1145/3583780.3615292

Published: 21 October 2023 Publication History

Abstract

The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.

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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
Rankovic NRankovic DIvanovic MKaljevic J(2024)Interpretable software estimation with graph neural networks and orthogonal array tunning methodInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10377861:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103778

Index Terms

Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges

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Published In

cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 21 October 2023

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CIKM '23

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

Sponsor:
sigir
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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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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
Rankovic NRankovic DIvanovic MKaljevic J(2024)Interpretable software estimation with graph neural networks and orthogonal array tunning methodInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10377861:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103778

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