Fetching the paper…
Reading the bibliography…
Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem.
K. Zhou, H. Wang, W. X. Zhao, Y. Zhu, S. Wang, F. Zhang, Z. Wang, and J.-R. Wen, “S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization,” in Proceedings of CIKM , 2020, pp. 1893–1902
1902
Earlier work this paper cites.
P. J. Huber, “Robust estimation of a location parameter,” in Breakthroughs in statistics . Springer, 1992, pp. 492–518
1992
Earlier work this paper cites.
T. W. Smith, “Generational differences in musical preferences,” Popular Music & Society , vol. 18, no. 2, pp. 43–59, 1994
1994
Earlier work this paper cites.
A. LeBlanc, W. L. Sims, C. Siivola, and M. Obert, “Music style preferences of different age listeners,” Journal of Research in Music Education , vol. 44, no. 1, pp. 49–59, 1996
1996
Earlier work this paper cites.
M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,” Annual review of sociology , vol. 27, no. 1, pp. 415–444, 2001
2001
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of WWW , 2001, pp. 285–295
2001
Earlier work this paper cites.
G. Linden, B. Smith, and J. York, “Amazon. com recommendations: Item-to-item collaborative filtering,” IEEE Internet computing , vol. 7, no. 1, pp. 76–80, 2003
2003
Earlier work this paper cites.
H. Ma, H. Yang, M. R. Lyu, and I. King, “Sorec: social recommendation using probabilistic matrix factorization,” in Proceedings of CIKM , 2008, pp. 931–940
2008
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.
S. Rendle, “Factorization machines,” in Proceedings of ICDM . IEEE, 2010, pp. 995–1000
2010
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in Proceedings of WWW , 2010, pp. 811–820
2010
Earlier work this paper cites.
J. Tang, X. Hu, and H. Liu, “Social recommendation: a review,” Social Network Analysis and Mining , vol. 3, no. 4, pp. 1113–1133, 2013
2013
Earlier work this paper cites.
S. Kabbur, X. Ning, and G. Karypis, “Fism: factored item similarity models for top-n recommender systems,” in Proceedings of SIGKDD , 2013, pp. 659–667
2013
Earlier work this paper cites.
F. Ricci, L. Rokach, and B. Shapira, “Recommender systems: introduction and challenges,” in Recommender systems handbook . Springer, 2015, pp. 1–34
2015
Earlier work this paper cites.
Y. Koren and R. Bell, “Advances in collaborative filtering,” Recommender systems handbook , pp. 77–118, 2015
2015
Earlier work this paper cites.
S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “Autorec: Autoencoders meet collaborative filtering,” in Proceedings of WWW , 2015, pp. 111–112
2015
Earlier work this paper cites.
H. Wang, N. Wang, and D.-Y. Yeung, “Collaborative deep learning for recommender systems,” in Proceedings of SIGKDD , 2015, pp. 1235–1244
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of ICLR , 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of ICLR , 2015
2015
Earlier work this paper cites.
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based recommendations with recurrent neural networks,” in Proceedings of 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 Proceedings of RecSys , 2016, pp. 241–248
2016
Earlier work this paper cites.
D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional matrix factorization for document context-aware recommendation,” in Proceedings of RecSys , 2016, pp. 233–240
2016
Earlier work this paper cites.
R. He and J. McAuley, “Fusing similarity models with markov chains for sparse sequential recommendation,” in Proceedings of ICDM . IEEE, 2016, pp. 191–200
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of CVPR , 2016, pp. 770–778
2016
Cited alongside, same era.
Y. Gong and Q. Zhang, “Hashtag recommendation using attention-based convolutional neural network.” in Proceedings of IJCAI , 2016, pp. 2782–2788
2016
Cited alongside, same era.
F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of SIGKDD , 2016, pp. 353–362
2016
Cited alongside, same era.
J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, “Neural attentive session-based recommendation,” in Proceedings of CIKM , 2017, pp. 1419–1428
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” CSUR , vol. 52, no. 1, pp. 1–38, 2019
2019
Later among the works it cites.
J. Huang, Z. Ren, W. X. Zhao, G. He, J.-R. Wen, and D. Dong, “Taxonomy-aware multi-hop reasoning networks for sequential recommendation,” in Proceedings of WSDM , 2019, pp. 573–581
2019
Later among the works it cites.
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 Proceedings of IJCAI , 2019, pp. 4320–4326
2019
Later among the works it cites.
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 Proceedings of CIKM , 2019, pp. 1161–1170
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
C.-Y. Wu, A. Ahmed, A. Beutel, A. J. Smola, and H. Jing, “Recurrent recommender networks,” in Proceedings of WSDM , 2017, pp. 495–503
2017
Cited alongside, same era.
H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, “Deepfm: a factorization-machine based neural network for ctr prediction,” in Proceedings of IJCAI , 2017, pp. 1725–1731
2017
Cited alongside, same era.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of WWW , 2017, pp. 173–182
2017
Cited alongside, same era.
W.-C. Kang, C. Fang, Z. Wang, and J. McAuley, “Visually-aware fashion recommendation and design with generative image models,” in Proceedings of ICDM . IEEE, 2017, pp. 207–216
2017
Cited alongside, same era.
S. Wang, Y. Wang, J. Tang, K. Shu, S. Ranganath, and H. Liu, “What your images reveal: Exploiting visual contents for point-of-interest recommendation,” in Proceedings of WWW , 2017, pp. 391–400
2017
Cited alongside, same era.
R. He, W.-C. Kang, and J. McAuley, “Translation-based recommendation,” in Proceedings of RecSys , 2017, pp. 161–169
2017
Cited alongside, same era.
M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, “Personalizing session-based recommendations with hierarchical recurrent neural networks,” in Proceedings of RecSys , 2017, pp. 130–137
2017
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019, pp. 4171–4186
2019
Later among the works it cites.
2019
Later among the works it cites.
X. Chen, D. Liu, C. Lei, R. Li, Z.-J. Zha, and Z. Xiong, “Bert4sessrec: Content-based video relevance prediction with bidirectional encoder representations from transformer,” in Proceedings of SIGMM , 2019, pp. 2597–2601
2019
Later among the works it 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 Proceedings of CIKM , 2019, pp. 1441–1450
2019
Later among the works it cites.
P. Ren, Z. Chen, J. Li, Z. Ren, J. Ma, and M. De Rijke, “Repeatnet: A repeat aware neural recommendation machine for session-based recommendation,” in Proceedings of AAAI , vol. 33, no. 01, 2019, pp. 4806–4813
2019
Later among the works it cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Proceedings of NeurIPS , 2019, pp. 5753–5763
2019
Later among the works it cites.
J. Chen, Y. Wu, L. Fan, X. Lin, H. Zheng, S. Yu, and Q. Xuan, “N2vscdnnr: A local recommender system based on node2vec and rich information network,” IEEE Transactions on Computational Social Systems , vol. 6, no. 3, pp. 456–466, 2019
2019
Later among the works it cites.
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in Proceedings of WWW , 2019, pp. 417–426
2019
Later among the works it cites.
2020
Later among the works it cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” in Proceedings of NeurIPS , 2020
2020
Later among the works it cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” JMLR , vol. 21, pp. 1–67, 2020
2020
Later among the works it cites.
F. Yuan, X. He, A. Karatzoglou, and L. Zhang, “Parameter-efficient transfer from sequential behaviors for user modeling and recommendation,” in Proceedings of SIGIR , 2020, pp. 1469–1478
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Fang, D. Zhang, Y. Shu, and G. Guo, “Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations,” TOIS , vol. 39, no. 1, pp. 1–42, 2020
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 theWebConf , 2020, pp. 303–313
2020
Later among the works it cites.
T. Wolf, J. Chaumond, L. Debut, V. Sanh, C. Delangue, A. Moi, P. Cistac, M. Funtowicz, J. Davison, S. Shleifer et al. , “Transformers: State-of-the-art natural language processing,” in Proceedings of EMNLP (Demo) , 2020, pp. 38–45
2020
Later among the works it cites.