research-article

LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning

Authors:

Victor S. Sheng,

Pengpeng ZhaoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 7

Article No.: 177, Pages 1 - 24

https://doi.org/10.1145/3657302

Published: 19 June 2024 Publication History

Abstract

Graph collaborative filtering (GCF) has achieved exciting recommendation performance with its ability to aggregate high-order graph structure information. Recently, contrastive learning (CL) has been incorporated into GCF to alleviate data sparsity and noise issues. However, most of the existing methods employ random or manual augmentation to produce contrastive views that may destroy the original topology and amplify the noisy effects. We argue that such augmentation is insufficient to produce the optimal contrastive view, leading to suboptimal recommendation results. In this article, we proposed a Learnable Model Augmentation Contrastive Learning (LMACL) framework for recommendation, which effectively combines graph-level and node-level collaborative relations to enhance the expressiveness of collaborative filtering (CF) paradigm. Specifically, we first use the graph convolution network (GCN) as a backbone encoder to incorporate multi-hop neighbors into graph-level original node representations by leveraging the high-order connectivity in user-item interaction graphs. At the same time, we treat the multi-head graph attention network (GAT) as an augmentation view generator to adaptively generate high-quality node-level augmented views. Finally, joint learning endows the end-to-end training fashion. In this case, the mutual supervision and collaborative cooperation of GCN and GAT achieves learnable model augmentation. Extensive experiments on several benchmark datasets demonstrate that LMACL provides a significant improvement over the strongest baseline in terms of Recall and NDCG by 2.5%–3.8% and 1.6%–4.0%, respectively. Our model implementation code is available at https://github.com/LiuHsinx/LMACL.

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Cited By

Yuehgoh FDjebali STravers N(2024)Leveraging recommendations using a multiplex graph databaseInternational Journal of Web Information Systems10.1108/IJWIS-05-2024-013720:5(537-582)Online publication date: 25-Oct-2024
https://doi.org/10.1108/IJWIS-05-2024-0137
Arthur JZhou CShen XAmber-Doh ROsei-Kwakye JMantey E(2024)Temporal dual-target cross-domain recommendation framework for next basket recommendationDiscover Computing10.1007/s10791-024-09479-w27:1Online publication date: 18-Dec-2024
https://doi.org/10.1007/s10791-024-09479-w
Lin SYuan HLiu GXian XCui ZZhao P(2024)Feature-Adaptive Meets Domain-Specific Networks for Multi-domain RecommendationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_3(32-47)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0570-5_3

Index Terms

LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 7

August 2024

505 pages

EISSN:1556-472X

DOI:10.1145/3613689

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 19 June 2024

Online AM: 12 April 2024

Accepted: 06 April 2024

Revised: 19 February 2024

Received: 24 August 2023

Published in TKDD Volume 18, Issue 7

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National Natural Science Foundation of China
National Key Research and Development Program of China
Universities of Jiangsu Province
Suzhou Science and Technology Development Program
Priority Academic Program Development of Jiangsu Higher Education Institutions

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Cited By

Yuehgoh FDjebali STravers N(2024)Leveraging recommendations using a multiplex graph databaseInternational Journal of Web Information Systems10.1108/IJWIS-05-2024-013720:5(537-582)Online publication date: 25-Oct-2024
https://doi.org/10.1108/IJWIS-05-2024-0137
Arthur JZhou CShen XAmber-Doh ROsei-Kwakye JMantey E(2024)Temporal dual-target cross-domain recommendation framework for next basket recommendationDiscover Computing10.1007/s10791-024-09479-w27:1Online publication date: 18-Dec-2024
https://doi.org/10.1007/s10791-024-09479-w
Lin SYuan HLiu GXian XCui ZZhao P(2024)Feature-Adaptive Meets Domain-Specific Networks for Multi-domain RecommendationWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_3(32-47)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0570-5_3

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