Fetching the paper…
Reading the bibliography…
The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: bayesian personalized ranking from implicit feedback,” in UAI , 2009
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics,” J. Mach. Learn. Res. , 2012
2012
Earlier work this paper cites.
A. Mnih and K. Kavukcuoglu, “Learning word embeddings efficiently with noise-contrastive estimation,” in NIPS , 2013
2013
Earlier work this paper cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” in NIPS , 2014
2014
Earlier work this paper cites.
A. Graves, G. Wayne, and I. Danihelka, “Neural turing machines,” CoRR , vol. abs/1410.5401, 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research , 2014
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NIPS , 2014
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR , 2015
2015
Earlier work this paper cites.
J. J. McAuley, C. Targett, Q. Shi, and A. van den Hengel, “Image-based recommendations on styles and substitutes,” in SIGIR , 2015
2015
Earlier work this paper cites.
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” in ICLR , 2016
2016
Earlier work this paper cites.
B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, “Parallel recurrent neural network architectures for feature-rich session-based recommendations,” in RecSys , 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017
2017
Earlier work this paper cites.
T. Ebesu, B. Shen, and Y. Fang, “Collaborative memory network for recommendation systems,” in SIGIR , 2018
2018
Earlier work this paper cites.
W. Kang and J. J. McAuley, “Self-attentive sequential recommendation,” in ICDM , 2018
2018
Earlier work this paper cites.
J. Tang and K. Wang, “Personalized top-n sequential recommendation via convolutional sequence embedding,” in WSDM , 2018
2018
Earlier work this paper cites.
2018
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 SIGKDD , 2018
2018
Earlier work this paper cites.
D. Garg, P. Gupta, P. Malhotra, L. Vig, and G. Shroff, “Sequence and time aware neighborhood for session-based recommendations: STAN,” in SIGIR , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in ICLR , 2019
2019
Cited alongside, same era.
C. Ma, P. Kang, and X. Liu, “Hierarchical gating networks for sequential recommendation,” in SIGKDD , 2019
2019
Cited alongside, same era.
B. Poole, S. Ozair, A. van den Oord, A. Alemi, and G. Tucker, “On variational bounds of mutual information,” in ICML , 2019
C. Ma, L. Ma, Y. Zhang, J. Sun, X. Liu, and M. Coates, “Memory augmented graph neural networks for sequential recommendation,” in AAAI , 2020
2020
Later among the works it cites.
J. Ma, C. Zhou, H. Yang, P. Cui, X. Wang, and W. Zhu, “Disentangled self-supervision in sequential recommenders,” in SIGKDD , 2020
2020
Later among the works it cites.
A. Miech, J. Alayrac, L. Smaira, I. Laptev, J. Sivic, and A. Zisserman, “End-to-end learning of visual representations from uncurated instructional videos,” in CVPR , 2020
2020
Later among the works it cites.
R. Qiu, Z. Huang, J. Li, and H. Yin, “Exploiting cross-session information for session-based recommendation with graph neural networks,” ACM Trans. Inf. Syst. , vol. 38, no. 3, pp. 22:1–22:23, 2020
2020
Later among the works it cites.
R. Qiu, H. Yin, Z. Huang, and T. Chen, “GAG: global attributed graph neural network for streaming session-based recommendation,” in SIGIR , 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
R. Qiu, J. Li, Z. Huang, and H. Yin, “Rethinking the item order in session-based recommendation with graph neural networks,” in CIKM , 2019
2019
Cited alongside, same era.
W. Song, C. Shi, Z. Xiao, Z. Duan, Y. Xu, M. Zhang, and J. Tang, “Autoint: Automatic feature interaction learning via self-attentive neural networks,” in CIKM , 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
2019
Cited alongside, same era.
T. Zhang, P. Zhao, Y. Liu, V. S. Sheng, J. Xu, D. Wang, G. Liu, and X. Zhou, “Feature-level deeper self-attention network for sequential recommendation,” in IJCAI , 2019
2019
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton, “A simple framework for contrastive learning of visual representations,” in ICML , 2020
2020
Cited alongside, same era.
T. Chen, H. Yin, Q. V. H. Nguyen, W. Peng, X. Li, and X. Zhou, “Sequence-aware factorization machines for temporal predictive analytics,” in ICDE , 2020
2020
Cited alongside, same era.
T. Han, W. Xie, and A. Zisserman, “Memory-augmented dense predictive coding for video representation learning,” in ECCV , 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
L. Xia, C. Huang, Y. Xu, P. Dai, B. Zhang, and L. Bo, “Multiplex behavioral relation learning for recommendation via memory augmented transformer network,” in SIGIR , 2020
2020
Later among the works it cites.
K. Zhou, H. Wang, W. X. Zhao, Y. Zhu, S. Wang, F. Zhang, Z. Wang, and J. Wen, “Sˆ3-rec: Self-supervised learning for sequential recommendation with mutual information maximization,” in CIKM , 2020
2020
Later among the works it cites.
Y. Li, T. Chen, Y. Luo, H. Yin, and Z. Huang, “Discovering collaborative signals for next POI recommendation with iterative seq2graph augmentation,” in IJCAI , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Q. Tan, J. Zhang, N. Liu, X. Huang, H. Yang, J. Zhou, and X. Hu, “Dynamic memory based attention network for sequential recommendation,” in AAAI , 2021
2021
Closest in time.
2021
Closest in time.
X. Xia, H. Yin, J. Yu, Q. Wang, L. Cui, and X. Zhang, “Self-supervised hypergraph convolutional networks for session-based recommendation,” in AAAI , 2021
2021
Closest in time.
2021
Closest in time.
J. Yu, H. Yin, J. Li, Q. Wang, N. Q. V. Hung, and X. Zhang, “Self-supervised multi-channel hypergraph convolutional network for social recommendation,” in WWW , 2021
2021
Closest in time.
C. Zhou, J. Ma, J. Zhang, J. Zhou, and H. Yang, “Contrastive learning for debiased candidate generation in large-scale recommender systems,” in SIGKDD , 2021
2021
Closest in time.