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Sequential recommender systems (SRS) have become a research hotspot due to its power in modeling user dynamic interests and sequential behavioral patterns.
S. Kullback and R. A. Leibler, “On information and sufficiency,” The annals of mathematical statistics , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
C. Li, J. Peng, L. Yuan, G. Wang, X. Liang, L. Lin, and X. Chang, “Block-wisely supervised neural architecture search with knowledge distillation,” in CVPR , 2020, pp. 1989–1998
1998
Earlier work this paper cites.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” IJCV , vol. 40, no. 2, pp. 99–121, 2000
2000
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in WWW , 2001, pp. 285–295
2001
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
I. Cantador, P. Brusilovsky, and T. Kuflik, “2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011),” in RecSys . ACM, 2011
2011
Earlier work this paper cites.
2015
Earlier work this paper cites.
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” ICLR , 2016
2016
Earlier work this paper cites.
Y. K. Tan, X. Xu, and Y. Liu, “Improved recurrent neural networks for session-based recommendations,” in DLRS , 2016, pp. 17–22
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” in ICLR , 2017
2017
Earlier work this paper cites.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in WWW , 2017, pp. 173–182
2017
Earlier work this paper cites.
C. J. Maddison, A. Mnih, and Y. W. Teh, “The concrete distribution: A continuous relaxation of discrete random variables,” in ICLR , 2017
2017
Earlier work this paper cites.
W.-C. Kang and J. McAuley, “Self-attentive sequential recommendation,” in ICDM . IEEE, 2018, pp. 197–206
2018
Earlier work this paper cites.
J. Tang and K. Wang, “Ranking distillation: Learning compact ranking models with high performance for recommender system,” in KDD , 2018, pp. 2289–2298
2018
Earlier work this paper cites.
B. Hidasi and A. Karatzoglou, “Recurrent neural networks with top-k gains for session-based recommendations,” in CIKM , 2018, pp. 843–852
2018
Earlier work this paper cites.
J. Tang and K. Wang, “Personalized top-n sequential recommendation via convolutional sequence embedding,” in WSDM , 2018, pp. 565–573
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
H. Wang, H. Zhao, X. Li, and X. Tan, “Progressive blockwise knowledge distillation for neural network acceleration.” in IJCAI , 2018, pp. 2769–2775
2018
Cited alongside, same era.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean, “Efficient neural architecture search via parameter sharing,” in ICML , 2018
2018
Cited alongside, same era.
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, “Amc: Automl for model compression and acceleration on mobile devices,” in ECCV , 2018, pp. 784–800
2018
Cited alongside, same era.
L. Xiao, Y. Bahri, J. Sohl-Dickstein, S. Schoenholz, and J. Pennington, “Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks,” in International Conference on Machine Learning . PMLR, 2018, pp. 5393–5402
2018
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam,” 2018
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer, “Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search,” in CVPR , 2019, pp. 10 734–10 742
2019
Later among the works it cites.
M. Ludewig, N. Mauro, S. Latifi, and D. Jannach, “Performance comparison of neural and non-neural approaches to session-based recommendation,” in RecSys , 2019, pp. 462–466
2019
Later among the works it cites.
2020
Later among the works it cites.
L. Wu, S. Li, C.-J. Hsieh, and J. Sharpnack, “Sse-pt: Sequential recommendation via personalized transformer,” in Fourteenth ACM Conference on Recommender Systems , 2020, pp. 328–337
2020
Later among the works it cites.
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2018
Cited alongside, same era.
F. Yuan, A. Karatzoglou, I. Arapakis, J. M. Jose, and X. He, “A simple convolutional generative network for next item recommendation,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining , 2019, pp. 582–590
2019
Cited alongside, same era.
Y. Pan, F. He, and H. Yu, “A novel enhanced collaborative autoencoder with knowledge distillation for top-n recommender systems,” Neurocomputing , vol. 332, pp. 137–148, 2019
2019
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in ICLR , 2019
2019
Cited alongside, same era.
S. Xie, H. Zheng, C. Liu, and L. Lin, “Snas: stochastic neural architecture search,” in ICLR , 2019
2019
Cited alongside, same era.
F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,” in CIKM , 2019, pp. 1441–1450
2019
Cited alongside, same era.
P. M. Gabriel De Souza, D. Jannach, and A. M. Da Cunha, “Contextual hybrid session-based news recommendation with recurrent neural networks,” IEEE Access , vol. 7, pp. 169 185–169 203, 2019
2019
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 AAAI , 2019, pp. 346–353
2019
Cited alongside, same era.
C. Ma, L. Ma, Y. Zhang, J. Sun, X. Liu, and M. Coates, “Memory augmented graph neural networks for sequential recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 5045–5052
2020
Later among the works it cites.
2020
Later among the works it cites.
Y. Sun, F. Yuan, M. Yang, G. Wei, Z. Zhao, and D. Liu, “A generic network compression framework for sequential recommender systems,” in SIGIR , 2020
2020
Later among the works it cites.
J. Li, X. Liu, H. Zhao, R. Xu, M. Yang, and Y. Jin, “Bert-emd: Many-to-many layer mapping for bert compression with earth mover’s distance,” in EMNLP , 2020
2020
Later among the works it cites.
D. Liu, P. Cheng, Z. Dong, X. He, W. Pan, and Z. Ming, “A general knowledge distillation framework for counterfactual recommendation via uniform data,” in SIGIR , 2020, pp. 831–840
2020
Later among the works it cites.
S. Kang, J. Hwang, W. Kweon, and H. Yu, “De-rrd: A knowledge distillation framework for recommender system,” in CIKM , 2020, pp. 605–614
2020
Later among the works it cites.
X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu, “Tinybert: Distilling bert for natural language understanding,” in EMNLP , 2020
2020
Later among the works it cites.
S. I. Mirzadeh, M. Farajtabar, A. Li, N. Levine, A. Matsukawa, and H. Ghasemzadeh, “Improved knowledge distillation via teacher assistant,” in AAAI , vol. 34, no. 04, 2020, pp. 5191–5198
2020
Later among the works it cites.
A. Wan, X. Dai, P. Zhang, Z. He, Y. Tian, S. Xie, B. Wu, M. Yu, T. Xu, K. Chen et al. , “Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions,” in CVPR , 2020, pp. 12 965–12 974
2020
Later among the works it cites.
2020
Later among the works it cites.
F. Yuan, X. He, H. Jiang, G. Guo, J. Xiong, Z. Xu, and Y. Xiong, “Future data helps training: Modeling future contexts for session-based recommendation,” in Proceedings of The Web Conference 2020 , 2020, pp. 303–313
2020
Later among the works it cites.
J. Wang, F. Yuan, J. Chen, Q. Wu, C. Li, M. Yang, Y. Sun, and G. Zhang, “Stackrec: Efficient training of very deep sequential recommender models by iterative stacking,” Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval , 2021
2021
Closest in time.
L. Chen, F. Yuan, J. Yang, X. Ao, C. Li, and M. Yang, “A user-adaptive layer selection framework for very deep sequential recommender models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 5, 2021, pp. 3984–3991
2021
Closest in time.
F. Yuan, G. Zhang, A. Karatzoglou, J. Jose, B. Kong, and Y. Li, “One person, one model, one world: Learning continual user representation without forgetting,” Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021
2021
Closest in time.