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ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation

Authors:

Evgeny Kharlamov,

Jie TangAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 759 - 769

https://doi.org/10.1145/3543507.3583530

Published: 30 April 2023 Publication History

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Abstract

In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.

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Cited By

Yang FPeng D(2024)Spatio-Temporal Contrastive Heterogeneous Graph Attention Networks for Session-Based RecommendationMathematics10.3390/math1208119312:8(1193)Online publication date: 16-Apr-2024
https://doi.org/10.3390/math12081193
Lan WZhou GChen QWang WPan SPan YZhang S(2024)Contrastive Clustering Learning for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/369819243:1(1-23)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3698192
Liao WZhu YLi YZhang QOu ZLi X(2024)RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer RecommendationACM Transactions on Information Systems10.1145/367920043:1(1-26)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3679200
Show More Cited By

Index Terms

ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

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

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Published: 30 April 2023

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Natural Science Foundation of China

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yang FPeng D(2024)Spatio-Temporal Contrastive Heterogeneous Graph Attention Networks for Session-Based RecommendationMathematics10.3390/math1208119312:8(1193)Online publication date: 16-Apr-2024
https://doi.org/10.3390/math12081193
Lan WZhou GChen QWang WPan SPan YZhang S(2024)Contrastive Clustering Learning for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/369819243:1(1-23)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3698192
Liao WZhu YLi YZhang QOu ZLi X(2024)RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer RecommendationACM Transactions on Information Systems10.1145/367920043:1(1-26)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3679200
Ali ZQi GUllah IMohammed AKefalas PMuhammad K(2024)GLAMOR: Graph-based LAnguage MOdel embedding for citation RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688171(929-933)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688171
Zhang FShi SZhu YChen BCen YYu JChen YWang LZhao QCheng YHan TAn YZhang DTam WCao KPang YGuan XYuan HSong JLi XDong YTang JBaeza-Yates RBonchi F(2024)OAG-Bench: A Human-Curated Benchmark for Academic Graph MiningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672354(6214-6225)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672354
Zhou PCui YCao HSerra ESpezzano F(2024)A Power Method to Alleviate Over-smoothing for RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679553(3484-3493)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679553
Wang ZLi YDing BLi YWei ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645571(780-791)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645571
Zhang DGeng YGong WQi ZChen ZTang XShan YDong YTang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)RecDCL: Dual Contrastive Learning for RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645533(3655-3666)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645533
Ma GYang XGao LLian L(2024)Adaptive denoising graph contrastive learning with memory graph attention for recommendationNeurocomputing10.1016/j.neucom.2024.128595610(128595)Online publication date: Dec-2024
https://doi.org/10.1016/j.neucom.2024.128595
Zhou PCui YGuo XWei JCao H(2024)Phase-wise attention GCN for recommendation denoisingApplied Soft Computing10.1016/j.asoc.2024.111910163:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111910
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