Fetching the paper…
Reading the bibliography…
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently.
J. Chen, D. Lian, B. Jin, X. Huang, K. Zheng, and E. Chen, “Fast variational autoencoder with inverted multi-index for collaborative filtering,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 1944–1954
1954
Earlier work this paper cites.
R. Bhatia and C. Davis, “A cauchy-schwarz inequality for operators with applications,” Linear algebra and its applications , vol. 223, pp. 119–129, 1995
1995
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
S. Rendle, “Factorization machines,” in 2010 IEEE International conference on data mining . IEEE, 2010, pp. 995–1000
2010
Earlier work this paper cites.
2014
Earlier work this paper cites.
X. He and T.-S. Chua, “Neural factorization machines for sparse predictive analytics,” in Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval , 2017, pp. 355–364
2017
Earlier work this paper 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
Earlier work this paper cites.
D. Liang, R. G. Krishnan, M. D. Hoffman, and T. Jebara, “Variational autoencoders for collaborative filtering,” in Proceedings of the 2018 world wide web conference , 2018, pp. 689–698
2018
Earlier work this paper cites.
D. Liang, R. G. Krishnan, M. D. Hoffman, and T. Jebara, “Variational autoencoders for collaborative filtering,” in Proceedings of the 2018 world wide web conference , 2018, pp. 689–698
2018
Earlier work this paper cites.
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in The world wide web conference , 2019, pp. 417–426
2019
Earlier work this paper cites.
J. Ma, C. Zhou, P. Cui, H. Yang, and W. Zhu, “Learning disentangled representations for recommendation,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
Y. Sun, X. Wang, Z. Liu, J. Miller, A. A. Efros, and M. Hardt, “Test-time training for out-of-distribution generalization,” 2019
2019
Earlier work this paper cites.
J. Ma, C. Zhou, P. Cui, H. Yang, and W. Zhu, “Learning disentangled representations for recommendation,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020, pp. 639–648
2020
Earlier work this paper cites.
J. Ma, C. Zhou, H. Yang, P. Cui, X. Wang, and W. Zhu, “Disentangled self-supervision in sequential recommenders,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 483–491
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
X. Wang, H. Jin, A. Zhang, X. He, T. Xu, and T.-S. Chua, “Disentangled graph collaborative filtering,” in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval , 2020, pp. 1001–1010
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang, “Graph representation learning via graphical mutual information maximization,” in Proceedings of The Web Conference 2020 , 2020, pp. 259–270
2020
Earlier work this paper cites.
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in International Conference on Machine Learning . PMLR, 2020, pp. 4116–4126
2020
Earlier work this paper cites.
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” Advances in Neural Information Processing Systems , vol. 33, pp. 5812–5823, 2020
2020
Earlier work this paper cites.
Y. Saito, S. Yaginuma, Y. Nishino, H. Sakata, and K. Nakata, “Unbiased recommender learning from missing-not-at-random implicit feedback,” in Proceedings of the 13th International Conference on Web Search and Data Mining , 2020, pp. 501–509
2020
Cited alongside, same era.
Y. Liu, P. Kothari, B. Van Delft, B. Bellot-Gurlet, T. Mordan, and A. Alahi, “Ttt++: When does self-supervised test-time training fail or thrive?” Advances in Neural Information Processing Systems , vol. 34, pp. 21 808–21 820, 2021
2021
Cited alongside, same era.
H. Zhao, X. Yang, Z. Wang, E. Yang, and C. Deng, “Graph debiased contrastive learning with joint representation clustering,” in Proc. IJCAI , 2021, pp. 3434–3440
2021
Cited alongside, same era.
Y. Wang, C. Li, M. Li, W. Jin, Y. Liu, H. Sun, X. Xie, and J. Tang, “Localized graph collaborative filtering,” in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) . SIAM, 2022, pp. 540–548
2022
Cited alongside, same era.
X. Yang, C. Tan, Y. Liu, K. Liang, S. Wang, S. Zhou, J. Xia, S. Z. Li, X. Liu, and E. Zhu, “Convert: Contrastive graph clustering with reliable augmentation,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 319–327
2023
Later among the works it cites.
K. Liang, Y. Liu, S. Zhou, W. Tu, Y. Wen, X. Yang, X. Dong, and X. Liu, “Knowledge graph contrastive learning based on relation-symmetrical structure,” IEEE Transactions on Knowledge and Data Engineering , vol. 36, no. 1, pp. 226–238, 2023
2023
Later among the works it cites.
Y. Mo, Y. Chen, Y. Lei, L. Peng, X. Shi, C. Yuan, and X. Zhu, “Multiplex graph representation learning via dual correlation reduction,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 12, pp. 12 814–12 827, 2023
2023
Later among the works it cites.
M. Yin, H. Wang, W. Guo, Y. Liu, S. Zhang, S. Zhao, D. Lian, and E. Chen, “Dataset regeneration for sequential recommendation,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 3954–3965
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Wang, C. Li, Z. Liu, M. Li, J. Tang, X. Xie, L. Chen, and P. S. Yu, “An adaptive graph pre-training framework for localized collaborative filtering,” ACM Transactions on Information Systems , vol. 41, no. 2, pp. 1–27, 2022
2022
Cited alongside, same era.
Y. Wang, C. Li, Z. Liu, M. Li, J. Tang, X. Xie, L. Chen, and P. S. Yu, “An adaptive graph pre-training framework for localized collaborative filtering,” ACM Transactions on Information Systems , vol. 41, no. 2, pp. 1–27, 2022
2022
Cited alongside, same era.
W. Wang, X. Lin, F. Feng, X. He, M. Lin, and T.-S. Chua, “Causal representation learning for out-of-distribution recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 3562–3571
2022
Cited alongside, same era.
Y. He, Z. Wang, P. Cui, H. Zou, Y. Zhang, Q. Cui, and Y. Jiang, “Causpref: Causal preference learning for out-of-distribution recommendation,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 410–421
2022
Cited alongside, same era.
A. Ballas and C. Diou, “Multi-layer representation learning for robust ood image classification,” in Proceedings of the 12th Hellenic Conference on Artificial Intelligence , 2022, pp. 1–4
2022
Cited alongside, same era.
X. Yang, X. Hu, S. Zhou, X. Liu, and E. Zhu, “Interpolation-based contrastive learning for few-label semi-supervised learning,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–12, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
W. Xia, Q. Wang, Q. Gao, M. Yang, and X. Gao, “Self-consistent contrastive attributed graph clustering with pseudo-label prompt,” IEEE Transactions on Multimedia , 2022
2022
Cited alongside, same era.
2024
Closest in time.
Y. Liu, S. Zhu, J. Xia, Y. Ma, J. Ma, W. Zhong, X. Liu, S. Yu, and K. Zhang, “End-to-end learnable clustering for intent learning in recommendation,” in Proc. of NeurIPS , 2024
2024
Closest in time.
Y. Liu, S. Zhu, T. Yang, J. Ma, and W. Zhong, “Identify then recommend: Towards unsupervised group recommendation,” in Proc. of NeurIPS , 2024
2024
Closest in time.
X. Lin, W. Wang, Y. Li, S. Yang, F. Feng, Y. Wei, and T.-S. Chua, “Data-efficient fine-tuning for llm-based recommendation,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2024, pp. 365–374
2024
Closest in time.
X. Lin, W. Wang, Y. Li, F. Feng, S.-K. Ng, and T.-S. Chua, “Bridging items and language: A transition paradigm for large language model-based recommendation,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 1816–1826
2024
Closest in time.
X. Ren, L. Xia, Y. Yang, W. Wei, T. Wang, X. Cai, and C. Huang, “Sslrec: A self-supervised learning framework for recommendation,” in Proceedings of the 17th ACM International Conference on Web Search and Data Mining , 2024, pp. 567–575
2024
Closest in time.
X. Yang, H. Chang, Z. Lai, J. Yang, X. Li, Y. Lu, S. Wang, D. Yin, and E. Min, “Hyperbolic contrastive learning for cross-domain recommendation,” in Proceedings of the 33rd ACM International Conference on Information and Knowledge Management , 2024, pp. 2920–2929
2024
Closest in time.
A. Zhang, W. Ma, P. Wei, L. Sheng, and X. Wang, “General debiasing for graph-based collaborative filtering via adversarial graph dropout,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 3864–3875
2024
Closest in time.
Q. Zheng, X. Yang, S. Wang, X. An, and Q. Liu, “Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information,” Neural Networks , vol. 179, p. 106563, 2024
2024
Closest in time.
A. Zhang, W. Ma, J. Zheng, X. Wang, and T.-S. Chua, “Robust collaborative filtering to popularity distribution shift,” ACM Transactions on Information Systems , vol. 42, no. 3, pp. 1–25, 2024
2024
Closest in time.
2024
Closest in time.
B. Wang, J. Chen, C. Li, S. Zhou, Q. Shi, Y. Gao, Y. Feng, C. Chen, and C. Wang, “Distributionally robust graph-based recommendation system,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 3777–3788
2024
Closest in time.
J. Zhang, Y. Wang, X. Yang, and E. Zhu, “A fully test-time training framework for semi-supervised node classification on out-of-distribution graphs,” ACM Transactions on Knowledge Discovery from Data , 2024
2024
Closest in time.
J. Huang, Y. Mo, P. Hu, X. Shi, S. Yuan, Z. Zhang, and X. Zhu, “Exploring the role of node diversity in directed graph representation learning,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence , 2024
2024
Closest in time.
J. Huang, J. Shen, X. Shi, and X. Zhu, “On which nodes does gcn fail? enhancing gcn from the node perspective,” in Forty-first International Conference on Machine Learning , 2024
2024
Closest in time.
X. Yang, E. Min, K. Liang, Y. Liu, S. Wang, S. Zhou, H. Wu, X. Liu, and E. Zhu, “Graphlearner: Graph node clustering with fully learnable augmentation,” in Proceedings of the 32nd ACM International Conference on Multimedia , 2024, pp. 5517–5526
2024
Closest in time.
X. Yang, Y. Wang, Y. Liu, Y. Wen, L. Meng, S. Zhou, X. Liu, and E. Zhu, “Mixed graph contrastive network for semi-supervised node classification,” ACM Transactions on Knowledge Discovery from Data , 2024
2024
Closest in time.
Y. Mo, H. T. Shen, and X. Zhu, “Unsupervised multi-view graph representation learning with dual weight-net,” Information Fusion , p. 102669, 2024
2024
Closest in time.
J. Zhang, Y. Wang, X. Yang, S. Wang, Y. Feng, Y. Shi, R. Ren, E. Zhu, and X. Liu, “Test-time training on graphs with large language models (llms),” in Proceedings of the 32nd ACM International Conference on Multimedia , 2024, pp. 2089–2098
2098
Closest in time.