short-paper

EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems

Authors:

Yingxue ZhangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1885 - 1889

https://doi.org/10.1145/3477495.3531770

Published: 07 July 2022 Publication History

Abstract

Graph Convolutional Neural Networks (GNN) based recommender systems are state-of-the-art since they can capture the high order collaborative signals between users and items. However, they suffer from the feature leakage problem since label information determined by edges can be leaked into node embeddings through the GNN aggregation procedure guided by the same set of edges, leading to poor generalization. We propose the accurate removal algorithm to generate the final embedding. For each edge, the embeddings of the two end nodes are evaluated on a graph with that edge removed. We devise an algebraic trick to efficiently compute this procedure without explicitly constructing separate graphs for the LightGCN model. Experiments on four datasets demonstrate that our algorithm can perform better on datasets with sparse interactions, while the training time is significantly reduced.

Supplementary Material

MP4 File (SIGIR22-sp1288.mp4)

We elaborate on the issue of feature leakage problem in the training procedure of GNN-based recommender systems. We demonstrate the advantages and disadvantages of two motivative solutions and present our main idea of combining their strengths of them.

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Cited By

Li JShi JZhang JLu YLi QYu CZhang S(2024)Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/367498218:8(1-28)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3674982
Mao XLiu YQi LDuan LXu XZhang XDou WBeheshti AZhou XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for MultimediaProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680788(5614-5622)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680788
Du DLai PWang YLiao DChen M(2024)AKGNN: Attribute Knowledge Graph Neural Networks Recommendation for Corporate Volunteer ActivitiesIEEE Transactions on Big Data10.1109/TBDATA.2024.345376110:6(720-730)Online publication date: Dec-2024
https://doi.org/10.1109/TBDATA.2024.3453761
Show More Cited By

Index Terms

EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
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Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
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Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Li JShi JZhang JLu YLi QYu CZhang S(2024)Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation SystemACM Transactions on Knowledge Discovery from Data10.1145/367498218:8(1-28)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3674982
Mao XLiu YQi LDuan LXu XZhang XDou WBeheshti AZhou XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for MultimediaProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680788(5614-5622)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680788
Du DLai PWang YLiao DChen M(2024)AKGNN: Attribute Knowledge Graph Neural Networks Recommendation for Corporate Volunteer ActivitiesIEEE Transactions on Big Data10.1109/TBDATA.2024.345376110:6(720-730)Online publication date: Dec-2024
https://doi.org/10.1109/TBDATA.2024.3453761
Lin SHong JLang BHuang L(2023)DAG: Dual Attention Graph Representation Learning for Node ClassificationMathematics10.3390/math1117369111:17(3691)Online publication date: 28-Aug-2023
https://doi.org/10.3390/math11173691
Gao YGong LWang CWang TLi XZhou X(2023)Algorithm/Hardware Co-Optimization for Sparsity-Aware SpMM Acceleration of GNNsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.328171442:12(4763-4776)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3281714
Liu YLiu J(2023)A surrogate evolutionary neural architecture search algorithm for graph neural networksApplied Soft Computing10.1016/j.asoc.2023.110485144:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110485
Shen TZhang YWang JZhang X(2023)Graphs get personal: learning representation with contextual pretraining for collaborative filteringApplied Intelligence10.1007/s10489-023-05144-953:24(30416-30430)Online publication date: 17-Nov-2023
https://dl.acm.org/doi/10.1007/s10489-023-05144-9
Hao QWang CXiao YLin H(2022)IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationshipsApplied Intelligence10.1007/s10489-022-04215-753:11(14668-14689)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10489-022-04215-7

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