research-article

A Contrastive Learning Framework for Dual-Target Cross-Domain Recommendation

Authors:

Wei HeAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6332 - 6339

https://doi.org/10.1145/3581783.3612250

Published: 27 October 2023 Publication History

Abstract

Cross-Domain Recommendation (CDR) is proposed to address the long-standing data sparsity problem in recommender systems (RSs). Traditional CDR only leverages relatively richer information from an auxiliary domain to improve the performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users to achieve knowledge transfer between domains. We argue that the existing methods face two challenges: (1) how to learn more representative user and item embeddings in each domain, and (2) in the case of a small number of common users in real-world datasets, how to achieve better knowledge transfer. To address these challenges, in this paper, we propose a contrastive learning (CL) framework, called CL-DTCDR. In CL-DTCDR, we first design a CL task in each domain to learn more representative user and item embeddings. Then, we further construct positive pairs of the user and her/his most similar user between domains to optimize user embeddings. By two CL tasks, CL-DTCDR effectively improves performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that CL-DTCDR significantly outperforms the state-of-the-art approaches.

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Cited By

Wang LXu MZhang QShi YWu Q(2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618:COnline publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.129133
Mao QLiu QLi ZWu LLv BZhang ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Cross-reconstructed Augmentation for Dual-target Cross-domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657902(2352-2356)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657902
Wang LSang LZhang QWu QXu M(2024)A privacy-preserving framework with multi-modal data for cross-domain recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112529304(112529)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112529
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Index Terms

A Contrastive Learning Framework for Dual-Target Cross-Domain Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

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Publication History

Published: 27 October 2023

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Fundamental Research Funds for the Central Universities
Shanghai Science and Technology Commission
National Science Fund for Young Scholars

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Wang LXu MZhang QShi YWu Q(2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618:COnline publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.129133
Mao QLiu QLi ZWu LLv BZhang ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Cross-reconstructed Augmentation for Dual-target Cross-domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657902(2352-2356)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657902
Wang LSang LZhang QWu QXu M(2024)A privacy-preserving framework with multi-modal data for cross-domain recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112529304(112529)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112529
Xu JGan MZhang HZhang S(2024)IDC-CDR: Cross-domain Recommendation based on Intent Disentanglement and Contrast LearningInformation Processing & Management10.1016/j.ipm.2024.10387161:6(103871)Online publication date: Nov-2024
https://doi.org/10.1016/j.ipm.2024.103871
Zang TZhu YZhang RZhu JTang F(2024)Explicitly modeling relationships between domain-specific and domain-invariant interests for cross-domain recommendationWorld Wide Web10.1007/s11280-024-01305-z27:6Online publication date: 28-Oct-2024
https://doi.org/10.1007/s11280-024-01305-z

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