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

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.

References

[1]
Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In WWW. 1341--1350.
[2]
Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. 2007. Cross-domain mediation in collaborative filtering. In ICUM. 355--359.
[3]
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, Vol. 33 (2020), 9912--9924.
[4]
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In CVPR. 9650--9660.
[5]
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a. A simple framework for contrastive learning of visual representations. In ICML. 1597--1607.
[6]
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020b. Big self-supervised models are strong semi-supervised learners. NeurIPS, Vol. 33 (2020), 22243--22255.
[7]
Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In CVPR. 15750--15758.
[8]
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In WWW. 2172--2182.
[9]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[10]
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. 2020. Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS, Vol. 33 (2020), 21271--21284.
[11]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. 855--864.
[12]
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738.
[13]
Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507--517.
[14]
Wei He, Guohao Sun, Jinhu Lu, and Xiu Susie Fang. 2023. Candidate-aware Graph Contrastive Learning for Recommendation. In SIGIR. 1670--1679.
[15]
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. Trirank: Review-aware explainable recommendation by modeling aspects. In CIKM. 1661--1670.
[16]
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. Nais: Neural attentive item similarity model for recommendation. TKDE, Vol. 30, 12 (2018), 2354--2366.
[17]
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR. 549--558.
[18]
Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In CIKM. 667--676.
[19]
SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In CIKM. 1563--1572.
[20]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[21]
Nikos Komodakis and Spyros Gidaris. 2018. Unsupervised representation learning by predicting image rotations. In ICLR.
[22]
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD. 426--434.
[23]
Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188--1196.
[24]
Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, and Hwanjo Yu. 2021. Bootstrapping user and item representations for one-class collaborative filtering. In SIGIR. 317--326.
[25]
Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In WSDM. 331--339.
[26]
Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW. 2320--2329.
[27]
Jinhu Lu, Guohao Sun, Xiu Fang, Jian Yang, and Wei He. 2023. A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation. In DASFAA. 297--313.
[28]
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In IJCAI, Vol. 17. 2464--2470.
[29]
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In ACL System Demonstrations. 55--60.
[30]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).
[31]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[32]
Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive multiview coding. In ECCV. 776--794.
[33]
Chunyu Wei, Jian Liang, Di Liu, and Fei Wang. 2022. Contrastive Graph Structure Learning via Information Bottleneck for Recommendation. NeurIPS, Vol. 35, 20407--20420.
[34]
Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan Li, Xuanping Li, and Tat-Seng Chua. 2021. Contrastive learning for cold-start recommendation. In ACM MM. 5382--5390.
[35]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.
[36]
Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2022. Contrastive cross-domain recommendation in matching. In SIGKDD. 4226--4236.
[37]
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In IJCAI, Vol. 17. 3203--3209.
[38]
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, and Evan Ettinger. 2021. Self-supervised learning for large-scale item recommendations. In CIKM. 4321--4330.
[39]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022a. Are graph augmentations necessary? simple graph contrastive learning for recommendation. In SIGIR. 1294--1303.
[40]
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, and Zi Huang. 2022b. Self-Supervised Learning for Recommender Systems: A Survey. arXiv preprint arXiv:2203.15876 (2022).
[41]
Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. Dtcdr: A framework for dual-target cross-domain recommendation. In CIKM. 1533--1542.
[42]
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation. In IJCAI. 3001--3008.
[43]
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. arXiv preprint arXiv:2103.01696 (2021).
[44]
Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized transfer of user preferences for cross-domain recommendation. In WSDM. 1507--1515.

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  • (2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618:COnline publication date: 14-Feb-2025
  • (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
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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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 27 October 2023

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    Author Tags

    1. contrastive learning
    2. cross-domain recommendation
    3. recommendation system

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    MM '23: The 31st ACM International Conference on Multimedia
    October 29 - November 3, 2023
    Ottawa ON, Canada

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    View all
    • (2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618:COnline publication date: 14-Feb-2025
    • (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
    • (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
    • (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
    • (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

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