research-article

AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering

Authors:

Yihua HuangAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1076 - 1085

https://doi.org/10.1145/3539618.3591632

Published: 18 July 2023 Publication History

Abstract

Graph collaborative filtering has achieved great success in capturing users' preferences over items. Despite effectiveness, graph neural network (GNN)-based methods suffer from data sparsity in real scenarios. Recently, contrastive learning (CL) has been used to address the problem of data sparsity. However, most CL-based methods only leverage the original user-item interaction graph to construct the CL task, lacking the explicit exploitation of the higher-order information (i.e., user-user and item-item relationships). Even for the CL-based method that uses the higher-order information, the reception field of the higher-order information is fixed and regardless of the difference between nodes. In this paper, we propose a novel adaptive multi-view fusion contrastive learning framework, named AdaMCL, for graph collaborative filtering. To exploit the higher-order information more accurately, we propose an adaptive fusion strategy to fuse the embeddings learned from the user-item and user-user graphs. Moreover, we propose a multi-view fusion contrastive learning paradigm to construct effective CL tasks. Besides, to alleviate the noisy information caused by aggregating higher-order neighbors, we propose a layer-level CL task. Extensive experimental results reveal that AdaMCL is effective and outperforms existing collaborative filtering models significantly.

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Index Terms

AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Published: 18 July 2023

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National Natural Science Foundation of China
Key Research and Development Program of Jiangsu Province
Natural Science Foundation of Jiangsu Province
Open Research Projects of Zhejiang Lab

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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https://doi.org/10.1016/j.neunet.2024.107001
Tao QLiu CXia YXu YLi L(2025)Adaptive multi-graph contrastive learning for bundle recommendationNeural Networks10.1016/j.neunet.2024.106832181(106832)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106832
Yu CZhao JWu XLuo YXiao Y(2025)TPGRec: Text-enhanced and popularity-smoothing graph collaborative filtering for long-tail item recommendationNeurocomputing10.1016/j.neucom.2025.129539626(129539)Online publication date: Apr-2025
https://doi.org/10.1016/j.neucom.2025.129539
Wei HWang JJi YGuang MYan C(2025)Hierarchical neighbor-enhanced graph contrastive learning for recommendationKnowledge-Based Systems10.1016/j.knosys.2025.113263(113263)Online publication date: Mar-2025
https://doi.org/10.1016/j.knosys.2025.113263
Xiao WZhou Q(2025)PT4Rec: a universal prompt-tuning framework for graph contrastive learning-based recommendationsMachine Learning10.1007/s10994-024-06658-0114:3Online publication date: 6-Feb-2025
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Dose YHaruta SHara T(2024)INCL: A Graph-based Recommendation Model Using Contrastive Learning for Inductive ScenarioJournal of Information Processing10.2197/ipsjjip.32.92932(929-937)Online publication date: 2024
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