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Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem.
Relational learning via collective matrix factorization
Singh, A. P.; and Gordon, G. J. 2008 · 2008
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BPR: Bayesian personalized ranking from implicit feedback
Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2012 · 2012
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Representation learning: A review and new perspectives
Bengio, Y.; Courville, A.; and Vincent, P. 2013 · 2013
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X.; Duan, Y.; Houthooft, R.; Schulman, J.; Sutskever, I.; and Abbeel, P. 2016 · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M.; Mohamed, S.; and Lerchner, A. 2016 · 2016
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Fuzzy-information-based robustness of interconnected networks against attacks and failures
Zhu, Q.; Zhu, Z.; Wang, Y.; and Yu, H. 2016 · 2016
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Collaborative metric learning
Hsieh, C.-K.; Yang, L.; Cui, Y.; Lin, T.-Y.; Belongie, S.; and Estrin, D. 2017 · 2017
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Cross-domain recommendation: An embedding and mapping approach
Man, T.; Shen, H.; Jin, X.; and Cheng, X. 2017 · 2017
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Conet: Collaborative cross networks for cross-domain recommendation
Hu, G.; Zhang, Y.; and Yang, Q. 2018 · 2018
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Semi-supervised learning for cross-domain recommendation to cold-start users
Kang, S.; Hwang, J.; Lee, D.; and Yu, H. 2019 · 2019
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Neural graph collaborative filtering
Wang, X.; He, X.; Wang, M.; Feng, F.; and Chua, T.-S. 2019 · 2019
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Improving disentangled text representation learning with information-theoretic guidance
Cheng, P.; Min, M. R.; Shen, D.; Malon, C.; Zhang, Y.; Li, Y.; and Carin, L. 2020 · 2020
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Ddtcdr: Deep dual transfer cross domain recommendation
Li, P.; and Tuzhilin, A. 2020 · 2020
Cited alongside, same era.
Attributed network embedding based on mutual information estimation
Liang, X.; Li, D.; and Madden, A. 2020 · 2020
Cited alongside, same era.
Cross domain recommendation via bi-directional transfer graph collaborative filtering networks
Liu, M.; Li, J.; Li, G.; and Pan, P. 2020 · 2020
Cited alongside, same era.
STR-SA: Session-based thread recommendation for online course forum with self-attention
Zhang, M.; Liu, S.; and Wang, Y. 2020 · 2020
Cited alongside, same era.
Disentangled contrastive learning on graphs
Li, H.; Wang, X.; Zhang, Z.; Yuan, Z.; Li, H.; and Zhu, W. 2021 · 2021
Personalized transfer of user preferences for cross-domain recommendation
Zhu, Y.; Tang, Z.; Liu, Y.; Zhuang, F.; Xie, R.; Zhang, X.; Lin, L.; and He, Q. 2022 · 2022
Later among the works it cites.
Towards universal cross-domain recommendation
Cao, J.; Li, S.; Yu, B.; Guo, X.; Liu, T.; and Wang, B. 2023 · 2023
Later among the works it cites.
Disentangled representations learning for multi-target cross-domain recommendation
Guo, X.; Li, S.; Guo, N.; Cao, J.; Liu, X.; Ma, Q.; Gan, R.; and Zhao, Y. 2023 · 2023
Later among the works it cites.
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Qin, Y.; Wang, Y.; Sun, F.; Ju, W.; Hou, X.; Wang, Z.; Cheng, J.; Lei, J.; and Zhang, M. 2023 · 2023
Later among the works it cites.
Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation
Liu, J.; Sun, L.; Nie, W.; Jing, P.; and Su, Y. 2024 · 2024
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Cited alongside, same era.
Towards source-aligned variational models for cross-domain recommendation
Salah, A.; Tran, T. B.; and Lauw, H. 2021 · 2021
Cited alongside, same era.
Transfer-meta framework for cross-domain recommendation to cold-start users
Zhu, Y.; Ge, K.; Zhuang, F.; Xie, R.; Xi, D.; Zhang, X.; Lin, L.; and He, Q. 2021 · 2021
Cited alongside, same era.
Cross-domain meta-learner for cold-start recommendation
Guan, R.; Pang, H.; Giunchiglia, F.; Liang, Y.; and Feng, X. 2022 · 2022
Cited alongside, same era.
Kernel-based substructure exploration for next POI recommendation
Ju, W.; Qin, Y.; Qiao, Z.; Luo, X.; Wang, Y.; Fu, Y.; and Zhang, M. 2022 · 2022
Cited alongside, same era.
Disencdr: Learning disentangled representations for cross-domain recommendation
Cao, J.; Lin, X.; Cong, X.; Ya, J.; Liu, T.; and Wang, B. 2022a
Cited in the paper.
Cross-domain recommendation to cold-start users via variational information bottleneck
Cao, J.; Sheng, J.; Cong, X.; Liu, T.; and Wang, B. 2022b
Cited in the paper.
Decoupled contrastive multi-view clustering with high-order random walks
Lu, Y.; Lin, Y.; Yang, M.; Peng, D.; Hu, P.; and Peng, X. 2024 · 2024
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Graph-Enhanced Prompt Learning for Personalized Review Generation
Qu, X.; Wang, Y.; Li, Z.; and Gao, J. 2024 · 2024
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DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Wang, Y.; Luo, X.; Chen, C.; Hua, X.-S.; Zhang, M.; and Ju, W. 2024 · 2024
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Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction
Li, B.; Yang, Q.; and Xue, X. 2009 · 2057
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Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
Li, H.; Ma, W.; Sun, P.; Li, J.; Yin, C.; He, Y.; Xu, G.; Zhang, M.; and Ma, S. 2024a · 2090
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