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Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.
B. Li, Q. Yang, and X. Xue, “Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction,” in Twenty-First international joint conference on artificial intelligence , 2009
2009
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B. Li, Q. Yang, and X. Xue, “Transfer learning for collaborative filtering via a rating-matrix generative model,” in Proceedings of the 26th annual international conference on machine learning , 2009, pp. 617–624
2009
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W. Pan, E. Xiang, N. Liu, and Q. Yang, “Transfer learning in collaborative filtering for sparsity reduction,” in Proceedings of the AAAI conference on artificial intelligence , vol. 24, no. 1, 2010, pp. 230–235
2010
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W. Pan and Q. Yang, “Transfer learning in heterogeneous collaborative filtering domains,” Artificial intelligence , vol. 197, pp. 39–55, 2013
2013
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P. Cremonesi and M. Quadrana, “Cross-domain recommendations without overlapping data: Myth or reality?” in Proceedings of the 8th ACM Conference on Recommender systems , 2014, pp. 297–300
2014
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S. Rendle, C. Freudenthaler, Z. Gantner, and L. B. Schmidt-Thieme, “Bayesian personalized ranking from implicit feedback,” in Proc. of Uncertainty in Artificial Intelligence , 2014, pp. 452–461
2014
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H. Yin, B. Cui, Z. Huang, W. Wang, X. Wu, and X. Zhou, “Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation,” in Proceedings of the 23rd ACM International Conference on Multimedia , 2015, p. 819–822
2015
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Y.-F. Liu, C.-Y. Hsu, and S.-H. Wu, “Non-linear cross-domain collaborative filtering via hyper-structure transfer,” in International Conference on Machine Learning , 2015, pp. 1190–1198
2015
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H. Yin and B. Cui, Spatio-temporal recommendation in social media . Springer, 2016
2016
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B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , 2016
2016
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
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X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 173–182
2017
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M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, “Personalizing session-based recommendations with hierarchical recurrent neural networks,” in proceedings of the Eleventh ACM Conference on Recommender Systems , 2017, pp. 130–137
2017
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J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, “Neural attentive session-based recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 1419–1428
2017
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W.-C. Kang and J. McAuley, “Self-attentive sequential recommendation,” in 2018 IEEE international conference on data mining (ICDM) , 2018, pp. 197–206
2018
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J. Tang and K. Wang, “Personalized top-n sequential recommendation via convolutional sequence embedding,” in Proceedings of the eleventh ACM international conference on web search and data mining , 2018, pp. 565–573
2018
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H. Yin, Q. Wang, K. Zheng, Z. Li, J. Yang, and X. Zhou, “Social influence-based group representation learning for group recommendation,” in 2019 IEEE 35th International Conference on Data Engineering (ICDE) , 2019, pp. 566–577
2019
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S. Jiang, Z. Ding, and Y. Fu, “Heterogeneous recommendation via deep low-rank sparse collective factorization,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 5, pp. 1097–1111, 2019
2019
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C. Wang, M. Niepert, and H. Li, “Recsys-dan: discriminative adversarial networks for cross-domain recommender systems,” IEEE transactions on neural networks and learning systems , vol. 31, no. 8, pp. 2731–2740, 2019
2019
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C. Gao, X. Chen, F. Feng, K. Zhao, X. He, Y. Li, and D. Jin, “Cross-domain recommendation without sharing user-relevant data,” in The world wide web conference , 2019, pp. 491–502
2019
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D. Perera and R. Zimmermann, “Cngan: Generative adversarial networks for cross-network user preference generation for non-overlapped users,” in The World Wide Web Conference , 2019, pp. 3144–3150
2019
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H. Kanagawa, H. Kobayashi, N. Shimizu, Y. Tagami, and T. Suzuki, “Cross-domain recommendation via deep domain adaptation,” in Advances in Information Retrieval , Cham, 2019, pp. 20–29
2019
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F. Yuan, L. Yao, and B. Benatallah, “Darec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns,” in IJCAI , 2019
2019
Cited alongside, same era.
F. Zhu, C. Chen, Y. Wang, G. Liu, and X. Zheng, “Dtcdr: A framework for dual-target cross-domain recommendation,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 1533–1542
2019
Cited alongside, same era.
J. Manotumruksa, D. Rafailidis, C. Macdonald, and I. Ounis, “On cross-domain transfer in venue recommendation,” in European Conference on Information Retrieval , 2019, pp. 443–456
2019
Cited alongside, same era.
F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, and A. Miller, “Language models as knowledge bases?” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , Nov. 2019, pp. 2463–2473
2019
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Aug. 2021, pp. 4582–4597
2021
Later among the works it cites.
L. Guo, J. Zhang, T. Chen, X. Wang, and H. Yin, “Reinforcement learning-enhanced shared-account cross-domain sequential recommendation,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
Later among the works it cites.
W. Liu, X. Zheng, M. Hu, and C. Chen, “Collaborative filtering with attribution alignment for review-based non-overlapped cross domain recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 1181–1190
2022
Later among the works it cites.
H. Zhang, X. Kong, and Y. Zhang, “Cross-domain collaborative recommendation without overlapping entities based on domain adaptation,” Multimedia Systems , pp. 1–17, 2022
2022
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Cited alongside, same era.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 346–353
2019
Cited alongside, same era.
M. Ma, P. Ren, Y. Lin, Z. Chen, J. Ma, and M. d. Rijke, “ π \pi -net: A parallel information-sharing network for shared-account cross-domain sequential recommendations,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2019, pp. 685–694
2019
Cited alongside, same era.
T. Chen, H. Yin, G. Ye, Z. Huang, Y. Wang, and M. Wang, “Try this instead: Personalized and interpretable substitute recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020, p. 891–900
2020
Cited alongside, same era.
Q. Wang, H. Yin, T. Chen, Z. Huang, H. Wang, Y. Zhao, and N. Q. Viet Hung, “Next point-of-interest recommendation on resource-constrained mobile devices,” in Proceedings of the Web conference 2020 , 2020, pp. 906–916
2020
Cited alongside, same era.
P. Li and A. Tuzhilin, “Ddtcdr: Deep dual transfer cross domain recommendation,” in Proceedings of the 13th International Conference on Web Search and Data Mining , 2020, pp. 331–339
2020
Cited alongside, same era.
M. Liu, J. Li, G. Li, and P. Pan, “Cross domain recommendation via bi-directional transfer graph collaborative filtering networks,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 885–894
2020
Cited alongside, same era.
F. Zhu, Y. Wang, C. Chen, G. Liu, and X. Zheng, “A graphical and attentional framework for dual-target cross-domain recommendation.” in IJCAI , 2020, pp. 3001–3008
2020
Cited alongside, same era.
Q. Cui, T. Wei, Y. Zhang, and Q. Zhang, “Herograph: A heterogeneous graph framework for multi-target cross-domain recommendation.” in ORSUM@ RecSys , 2020
2020
Cited alongside, same era.
Later among the works it cites.
J. Cao, X. Lin, X. Cong, J. Ya, T. Liu, and B. Wang, “Disencdr: Learning disentangled representations for cross-domain recommendation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, p. 267–277
2022
Later among the works it cites.
Y. Choi, J. Choi, T. Ko, H. Byun, and C.-K. Kim, “Review-based domain disentanglement without duplicate users or contexts for cross-domain recommendation,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , 2022, p. 293–303
2022
Later among the works it cites.
2022
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L. Guo, J. Zhang, L. Tang, T. Chen, L. Zhu, and H. Yin, “Time interval-enhanced graph neural network for shared-account cross-domain sequential recommendation,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Later among the works it cites.
J. Cao, X. Cong, J. Sheng, T. Liu, and B. Wang, “Contrastive cross-domain sequential recommendation,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , 2022, pp. 138–147
2022
Later among the works it cites.
M. Ma, P. Ren, Z. Chen, Z. Ren, L. Zhao, P. Liu, J. Ma, and M. de Rijke, “Mixed information flow for cross-domain sequential recommendations,” ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 16, no. 4, pp. 1–32, 2022
2022
Later among the works it cites.
C. Li, M. Zhao, H. Zhang, C. Yu, L. Cheng, G. Shu, B. Kong, and D. Niu, “Recguru: Adversarial learning of generalized user representations for cross-domain recommendation,” in Proceedings of the fifteenth ACM international conference on web search and data mining , 2022, pp. 571–581
2022
Later among the works it cites.
S. Geng, S. Liu, Z. Fu, Y. Ge, and Y. Zhang, “Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),” in Proceedings of the 16th ACM Conference on Recommender Systems , 2022, pp. 299–315
2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Wu, R. Xie, Y. Zhu, F. Zhuang, A. Xiang, X. Zhang, L. Lin, and Q. He, “Selective fairness in recommendation via prompts,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 2657–2662
2022
Later among the works it cites.
X. Xin, T. Pimentel, A. Karatzoglou, P. Ren, K. Christakopoulou, and Z. Ren, “Rethinking reinforcement learning for recommendation: A prompt perspective,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1347–1357
2022
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2022
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K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision , vol. 130, no. 9, pp. 2337–2348, 2022
2022
Later among the works it cites.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, pp. 195:1–195:35, 2023
2023
Closest in time.
L. Li, Y. Zhang, and L. Chen, “Personalized prompt learning for explainable recommendation,” ACM Transactions on Information Systems (TOIS) , 2023
2023
Closest in time.