research-article

Neural Graph Matching based Collaborative Filtering

Authors:

Sarah M. Erfani,

Junhao GanAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 849 - 858

https://doi.org/10.1145/3404835.3462833

Published: 11 July 2021 Publication History

Abstract

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.

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Cited By

Liu XHao YZhao LLiu GSheng VZhao P(2024)LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/365730218:7(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657302
Lin ZYan QLiu WWang SWang MTan YYang C(2024)Automatic Hypergraph Generation for Enhancing Recommendation With Sparse OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333808326(5680-5693)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3338083
Wu CChen YYeh Y(2024)A Deep Recommendation Model Considering the Impact of Time and Individual DiversityIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.327263311:2(2558-2569)Online publication date: Apr-2024
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Index Terms

Neural Graph Matching based Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

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Cited By

Liu XHao YZhao LLiu GSheng VZhao P(2024)LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/365730218:7(1-24)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3657302
Lin ZYan QLiu WWang SWang MTan YYang C(2024)Automatic Hypergraph Generation for Enhancing Recommendation With Sparse OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333808326(5680-5693)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3338083
Wu CChen YYeh Y(2024)A Deep Recommendation Model Considering the Impact of Time and Individual DiversityIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.327263311:2(2558-2569)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3272633
Yang BLiu YWu JBai FZheng MZheng J(2024)GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router NodesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.340252928:12(7588-7598)Online publication date: Dec-2024
https://doi.org/10.1109/JBHI.2024.3402529
Shen JLi DChen WXiao YKang GLiu JPeng Z(2024)Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00087(637-644)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00087
Zhang YChen JZou ZYao MZhang SXiao L(2024)CSIA-GCN: A Doctor Recommendation Model Based on Interactive Graph Convolutional Networks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650337(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650337
Li CKou YShen DNie TLi D(2024)Cross-Grained Neural Collaborative Filtering for RecommendationIEEE Access10.1109/ACCESS.2024.338437612(48853-48864)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3384376
Xu NHu JFang QXue DLi YQian S(2024)Tri-relational multi-faceted graph neural networks for automatic question taggingNeurocomputing10.1016/j.neucom.2024.127250576:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127250
Ma AYu YShi CGuo ZChua T(2024)Cross-view hypergraph contrastive learning for attribute-aware recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10370161:4Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103701
Nahta RChauhan GMeena YGopalani D(2024)CF-MGAN: Collaborative filtering with metadata-aware generative adversarial networks for top-N recommendationInformation Sciences10.1016/j.ins.2024.121337(121337)Online publication date: Aug-2024
https://doi.org/10.1016/j.ins.2024.121337
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