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Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications.
Å. Björck, “Numerics of gram-schmidt orthogonalization,” Linear Algebra and Its Applications , vol. 197, pp. 297–316, 1994
1994
Earlier work this paper cites.
C. D. Meyer, Matrix analysis and applied linear algebra . Siam, 2000, vol. 71
2000
Earlier work this paper cites.
D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Advances in neural information processing systems , 2001, pp. 556–562
2001
Earlier work this paper cites.
A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” in Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval . ACM, 2002, pp. 253–260
2002
Earlier work this paper cites.
E. P. Xing, M. I. Jordan, S. J. Russell, and A. Y. Ng, “Distance metric learning with application to clustering with side-information,” in Advances in neural information processing systems , 2003, pp. 521–528
2003
Earlier work this paper cites.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge & Data Engineering , no. 6, pp. 734–749, 2005
2005
Earlier work this paper cites.
L. Yang and R. Jin, “Distance metric learning: A comprehensive survey,” Michigan State Universiy , vol. 2, no. 2, p. 4, 2006
2006
Earlier work this paper cites.
C.-J. Lin, “On the convergence of multiplicative update algorithms for nonnegative matrix factorization,” IEEE Transactions on Neural Networks , vol. 18, no. 6, pp. 1589–1596, 2007
2007
Earlier work this paper cites.
A. P. Singh and G. J. Gordon, “Relational learning via collective matrix factorization,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2008, pp. 650–658
2008
Earlier work this paper cites.
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
Earlier work this paper cites.
E. Zhong, W. Fan, J. Peng, K. Zhang, J. Ren, D. Turaga, and O. Verscheure, “Cross domain distribution adaptation via kernel mapping,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2009
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , 2010
2010
Earlier work this paper cites.
F. Zhuang, P. Luo, Z. Shen, Q. He, Y. Xiong, Z. Shi, and H. Xiong, “Collaborative dual-plsa: mining distinction and commonality across multiple domains for text classification,” in Proceedings of the 19th ACM international conference on Information and knowledge management . ACM, 2010, pp. 359–368
2010
Earlier work this paper cites.
M. Khoshneshin and W. N. Street, “Collaborative filtering via euclidean embedding,” in Proceedings of the fourth ACM conference on Recommender systems , 2010, pp. 87–94
2010
Earlier work this paper cites.
H. Wang, H. Huang, F. Nie, and C. Ding, “Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval . ACM, 2011, pp. 933–942
2011
Earlier work this paper cites.
J. Wang, H. T. Do, A. Woznica, and A. Kalousis, “Metric learning with multiple kernels,” in Advances in neural information processing systems , 2011, pp. 1170–1178
2011
Earlier work this paper cites.
F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender systems handbook . Springer, 2011, pp. 1–35
2011
Earlier work this paper cites.
I. Fernández-Tobías, I. Cantador, M. Kaminskas, and F. Ricci, “Cross-domain recommender systems: A survey of the state of the art,” 2012
2012
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, W. Cheng, X. Zhang, and W. Wang, “Dual transfer learning,” in Proceedings of the 2012 SIAM International Conference on Data Mining . SIAM, 2012
2012
Earlier work this paper cites.
B. Kulis et al. , “Metric learning: A survey,” Foundations and trends in machine learning , vol. 5, no. 4, pp. 287–364, 2012
2012
Earlier work this paper cites.
D. Kedem, S. Tyree, F. Sha, G. R. Lanckriet, and K. Q. Weinberger, “Non-linear metric learning,” in Advances in neural information processing systems , 2012, pp. 2573–2581
2012
Earlier work this paper cites.
S. Chen, J. L. Moore, D. Turnbull, and T. Joachims, “Playlist prediction via metric embedding,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining , 2012, pp. 714–722
2012
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Advances in Neural Information Processing Systems , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 2951–2959
2012
Earlier work this paper cites.
L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu, “Personalized recommendation via cross-domain triadic factorization,” in Proceedings of the 22nd international conference on World Wide Web , 2013
2013
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation,” in Proceedings of the IEEE ICCV , 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
B. Loni, Y. Shi, M. Larson, and A. Hanjalic, “Cross-domain collaborative filtering with factorization machines,” in European conference on information retrieval . Springer, 2014, pp. 656–661
2014
Cited alongside, same era.
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
Cited alongside, same era.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Cited alongside, same era.
I. Cantador, I. Fernández-Tobías, S. Berkovsky, and P. Cremonesi, “Cross-domain recommender systems,” in Recommender systems handbook . Springer, 2015, pp. 919–959
2015
Cited alongside, same era.
N. Bansal, X. Chen, and Z. Wang, “Can we gain more from orthogonality regularizations in training deep networks?” in Advances in Neural Information Processing Systems , 2018, pp. 4261–4271
2018
Later among the works it cites.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 974–983
2018
Later among the works it cites.
Q. Wang, H. Yin, Z. Hu, D. Lian, H. Wang, and Z. Huang, “Neural memory streaming recommender networks with adversarial training,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 2467–2475
2018
Later among the works it cites.
I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schölkopf, “Wasserstein auto-encoders,” in 6th International Conference on Learning Representations (ICLR 2018) . OpenReview. net, 2018
2018
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S. Feng, X. Li, Y. Zeng, G. Cong, Y. M. Chee, and Q. Yuan, “Personalized ranking metric embedding for next new poi recommendation,” in Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
Cited alongside, same era.
H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative deep learning for recommender systems,” in Proceedings of the 21th ACM SIGKDD . ACM, 2015, pp. 1235–1244
2015
Cited alongside, same era.
S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “Autorec: Autoencoders meet collaborative filtering,” in Proceedings of the 24th International Conference on World Wide Web . ACM, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning , 2015, pp. 448–456
2015
Cited alongside, same era.
D. He, Y. Xia, T. Qin, L. Wang, N. Yu, T.-Y. Liu, and W.-Y. Ma, “Dual learning for machine translation,” in Advances in Neural Information Processing Systems , 2016, pp. 820–828
2016
Cited alongside, same era.
Y. Wu, C. DuBois, A. X. Zheng, and M. Ester, “Collaborative denoising auto-encoders for top-n recommender systems,” in Proceedings of the Ninth ACM WSDM , 2016
2016
Cited alongside, same era.
Later among the works it cites.
2018
Later among the works it cites.
I. Fernández-Tobías, I. Cantador, P. Tomeo, V. W. Anelli, and T. Di Noia, “Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization,” User Modeling and User-Adapted Interaction , vol. 29, no. 2, pp. 443–486, 2019
2019
Later among the works it cites.
Y. Wang, C. Feng, C. Guo, Y. Chu, and J.-N. Hwang, “Solving the sparsity problem in recommendations via cross-domain item embedding based on co-clustering,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining , 2019, pp. 717–725
2019
Later among the works it cites.
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
Later among the works it cites.
Q. Do, W. Liu, J. Fan, and D. Tao, “Unveiling hidden implicit similarities for cross-domain recommendation,” IEEE Transactions on Knowledge and Data Engineering , 2019
2019
Later among the works it cites.
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
Later among the works it cites.
C. Zhao, C. Li, and C. Fu, “Cross-domain recommendation via preference propagation graphnet,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 2165–2168
2019
Later among the works it cites.
H. Kanagawa, H. Kobayashi, N. Shimizu, Y. Tagami, and T. Suzuki, “Cross-domain recommendation via deep domain adaptation,” in European Conference on Information Retrieval . Springer, 2019, pp. 20–29
2019
Later among the works it cites.
W. Fu, Z. Peng, S. Wang, Y. Xu, and J. Li, “Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 94–101
2019
Later among the works it cites.
G. Hu, Y. Zhang, and Q. Yang, “Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text,” in The World Wide Web Conference , 2019, pp. 2822–2829
2019
Later among the works it cites.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys (CSUR) , vol. 52, no. 1, p. 5, 2019
2019
Later among the works it cites.
P. Li and A. Tuzhilin, “Latent multi-criteria ratings for recommendations,” in Proceedings of the 13th ACM Conference on Recommender Systems , 2019, pp. 428–431
2019
Later among the works it cites.
J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” 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) , 2019, pp. 188–197
2019
Later among the works it cites.
Q. Zhu, X. Zhou, Z. Song, J. Tan, and L. Guo, “Dan: Deep attention neural network for news recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 5973–5980
2019
Later among the works it cites.
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
Later among the works it cites.
T. Qin, Dual Learning . Springer Nature, 2020
2020
Later among the works it cites.
C. Zhao, C. Li, R. Xiao, H. Deng, and A. Sun, “Catn: Cross-domain recommendation for cold-start users via aspect transfer network,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020, pp. 229–238
2020
Later among the works it cites.
A. Krishnan, M. Das, M. Bendre, H. Yang, and H. Sundaram, “Transfer learning via contextual invariants for one-to-many cross-domain recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020, pp. 1081–1090
2020
Later among the works it cites.
L. Vinh Tran, Y. Tay, S. Zhang, G. Cong, and X. Li, “Hyperml: A boosting metric learning approach in hyperbolic space for recommender systems,” in Proceedings of the 13th International Conference on Web Search and Data Mining , 2020, pp. 609–617
2020
Later among the works it cites.
P. Li and A. Tuzhilin, “Learning latent multi-criteria ratings from user reviews for recommendations,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2020
2020
Later among the works it cites.
P. Li, M. Que, Z. Jiang, Y. Hu, and A. Tuzhilin, “Purs: Personalized unexpected recommender system for improving user satisfaction,” in Fourteenth ACM Conference on Recommender Systems , 2020, pp. 279–288
2020
Later among the works it cites.