Fetching the paper…
Reading the bibliography…
Scaling of neural networks has recently shown great potential to improve the model capacity in various fields.
H. Robbins and S. Monro, “A stochastic approximation method,” The annals of mathematical statistics , pp. 400–407, 1951
1951
Earlier work this paper cites.
P. Gage, “A new algorithm for data compression,” C Users Journal , vol. 12, no. 2, pp. 23–38, 1994
1994
Earlier work this paper cites.
H. N. Mhaskar, “Neural networks for optimal approximation of smooth and analytic functions,” Neural computation , vol. 8, no. 1, pp. 164–177, 1996
1996
Earlier work this paper cites.
M. O’Mahony, N. Hurley, N. Kushmerick, and G. Silvestre, “Collaborative recommendation: A robustness analysis,” TOIT , vol. 4, no. 4, pp. 344–377, 2004
2004
Earlier work this paper cites.
J. O’Donovan and B. Smyth, “Trust in recommender systems,” in IUI , 2005, pp. 167–174
2005
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in WWW , 2010, pp. 811–820
2010
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,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980 , 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” TiiS , vol. 5, no. 4, pp. 1–19, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
2017
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.
X. He, Z. He, X. Du, and T.-S. Chua, “Adversarial personalized ranking for recommendation,” in The 41st International ACM SIGIR conference on research & development in information retrieval , 2018, pp. 355–364
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in AAAI , vol. 33, no. 01, 2019, pp. 346–353
2019
Earlier work this paper cites.
J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” in EMNLP-IJCNLP , 2019, pp. 188–197
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
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 SIGIR , 2020, pp. 639–648
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” NeurIPS , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-art natural language processing,” in EMNLP . Online: Association for Computational Linguistics, Oct. 2020, pp. 38–45. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-demos.6
2020
Cited alongside, same era.
W. Yu, X. Lin, J. Ge, W. Ou, and Z. Qin, “Semi-supervised collaborative filtering by text-enhanced domain adaptation,” in KDD , 2020, pp. 2136–2144
2020
Cited alongside, same era.
J. Chang, C. Gao, Y. Zheng, Y. Hui, Y. Niu, Y. Song, D. Jin, and Y. Li, “Sequential recommendation with graph neural networks,” in SIGIR , 2021, pp. 378–387
2021
Cited alongside, same era.
Y. Li, T. Chen, P.-F. Zhang, and H. Yin, “Lightweight self-attentive sequential recommendation,” in CIKM , 2021, pp. 967–977
2021
2023
Closest in time.
Y. Hou, Z. He, J. McAuley, and W. X. Zhao, “Learning vector-quantized item representation for transferable sequential recommenders,” in WWW , 2023, pp. 1162–1171
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
X. Fan, Z. Liu, J. Lian, W. X. Zhao, X. Xie, and J.-R. Wen, “Lighter and better: low-rank decomposed self-attention networks for next-item recommendation,” in SIGIR , 2021, pp. 1733–1737
2021
Cited alongside, same era.
W. X. Zhao, S. Mu, Y. Hou, Z. Lin, Y. Chen, X. Pan, K. Li, Y. Lu, H. Wang, C. Tian et al. , “Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms,” in CIKM , 2021, pp. 4653–4664
2021
Cited alongside, same era.
2022
Cited alongside, same era.
X. Zhai, A. Kolesnikov, N. Houlsby, and L. Beyer, “Scaling vision transformers,” in CVPR , 2022, pp. 12 104–12 113
2022
Cited alongside, same era.
Y. Hou, S. Mu, W. X. Zhao, Y. Li, B. Ding, and J.-R. Wen, “Towards universal sequence representation learning for recommender systems,” in KDD , 2022, pp. 585–593
2022
Cited alongside, same era.
Z. Lin, C. Tian, Y. Hou, and W. X. Zhao, “Improving graph collaborative filtering with neighborhood-enriched contrastive learning,” in WWW , 2022, pp. 2320–2329
2022
Cited alongside, same era.
K. Zhou, H. Yu, W. X. Zhao, and J.-R. Wen, “Filter-enhanced mlp is all you need for sequential recommendation,” in WWW , 2022, pp. 2388–2399
2022
Cited alongside, same era.
Y. Hou, B. Hu, Z. Zhang, and W. X. Zhao, “Core: Simple and effective session-based recommendation within consistent representation space,” in SIGIR , 2022
2022
Cited alongside, same era.
2023
Closest in time.
O. Nov, N. Singh, and D. M. Mann, “Putting chatgpt’s medical advice to the (turing) test,” medRxiv , pp. 2023–01, 2023
2023
Closest in time.
K. Malinka, M. Peresíni, A. Firc, O. Hujnak, and F. Janus, “On the educational impact of chatgpt: Is artificial intelligence ready to obtain a university degree?” in ITiCSE , 2023, pp. 47–53
2023
Closest in time.
2023
Closest in time.
Z. Sun, “A short survey of viewing large language models in legal aspect,” arXiv:2303.09136 , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. Harte, W. Zorgdrager, P. Louridas, A. Katsifodimos, D. Jannach, and M. Fragkoulis, “Leveraging large language models for sequential recommendation,” in RecSys , 2023, pp. 1096–1102
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.