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Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings
Diederik P. Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning . PMLR, 1278–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016 · 2016
Earlier work this paper cites.
Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 191–200
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems . 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2016 · 2016
Earlier work this paper cites.
Variational inference using implicit distributions
Ferenc Huszár. 2017 · 2017
Earlier work this paper cites.
Denoising criterion for variational auto-encoding framework. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 31
Daniel Im Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning . 2391–2400
Lars M. Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017 · 2017
Cited alongside, same era.
Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 515–524
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017 · 2017
Cited alongside, same era.
Recurrent recommender networks. In Proceedings of the tenth ACM international conference on web search and data mining . 495–503
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017 · 2017
Cited alongside, same era.
Seqgan: Sequence generative adversarial nets with policy gradient. In Thirty-first AAAI conference on artificial intelligence
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Later among the works it cites.
Sequential recommender system based on hierarchical attention network. In IJCAI International Joint Conference on Artificial Intelligence
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018 · 2018
Later among the works it cites.
Learning disentangled representations for recommendation. In Advances in Neural Information Processing Systems . 5711–5722
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019 · 2019
Later among the works it cites.
Sequential variational autoencoders for collaborative filtering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 600–608
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi. 2019 · 2019
Later among the works it cites.
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Cfgan: A generic collaborative filtering framework based on generative adversarial networks. In Proceedings of the 27th ACM international conference on information and knowledge management . 137–146
Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018 · 2018
Cited alongside, same era.
Adversarial personalized ranking for recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 355–364
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018 · 2018
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Variational autoencoders for collaborative filtering. In Proceedings of the 2018 World Wide Web Conference . 689–698
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Cited alongside, same era.
Coupled Variational Recurrent Collaborative Filtering. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 335–343
Qingquan Song, Shiyu Chang, and Xia Hu. 2019 · 2019
Later among the works it cites.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Later among the works it cites.
Sequential Recommender Systems: Challenges, Progress and Prospects. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . 6332–6338
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet A. Orgun. 2019 · 2019
Later among the works it cites.
Recurrent convolutional neural network for sequential recommendation. In The World Wide Web Conference . 3398–3404
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S Sheng S. Sheng, Zhiming Cui, Xiaofang Zhou, and Hui Xiong. 2019 · 2019
Later among the works it cites.
Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019 · 2019
Later among the works it cites.
RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining . 528–536
Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020 · 2020
Later among the works it cites.