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Learning user representations is a vital technique toward effective user modeling and personalized recommender systems.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM . 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
Earlier work this paper cites.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018 · 1939
Earlier work this paper cites.
Lifelong robot learning
Sebastian Thrun and Tom M Mitchell. 1995 · 1995
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence . 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines. In ICML . 807–814
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 2333–2338
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally. 2015 · 2015
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.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang. 2016 · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Pixel Recurrent Neural Networks. In International Conference on Machine Learning . 1747–1756
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
Lambdafm: learning optimal ranking with factorization machines using lambda surrogates. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . ACM, 227–236
Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 355–364
Xiangnan He and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 565–574
Kan Ren, Jiarui Qin, Yuchen Fang, Weinan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, et al · 2019
Later among the works it cites.
Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 1703–1712
Jingyi Wang, Qiang Liu, Zhaocheng Liu, and Shu Wu. 2019 · 2019
Later among the works it cites.
A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . ACM, 582–590
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2019 · 2019
Later among the works it cites.
Filter grafting for deep neural networks. In CVPR . 6599–6607
Fanxu Meng, Hao Cheng, Ke Li, Zhixin Xu, Rongrong Ji, Xing Sun, and Guangming Lu. 2020 · 2020
Closest in time.
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Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017 · 2017
Cited alongside, same era.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights. In Proceedings of the European Conference on Computer Vision (ECCV) . 67–82
Arun Mallya, Dillon Davis, and Svetlana Lazebnik. 2018 · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 7765–7773
Arun Mallya and Svetlana Lazebnik. 2018 · 2018
Cited alongside, same era.
Never-ending learning
Tom Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Bishan Yang, Justin Betteridge, Andrew Carlson, Bhavana Dalvi, Matt Gardner, Bryan Kisiel, et al · 2018
Cited alongside, same era.
Ader: Adaptively distilled exemplar replay towards continual learning for session-based recommendation. In Fourteenth ACM Conference on Recommender Systems . 408–413
Fei Mi, Xiaoyu Lin, and Boi Faltings. 2020 · 2020
Closest in time.
Pi Qi, Xiaoqiang Zhu, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, and Kun Gai. 2020 · 2020
Closest in time.
A Generic Network Compression Framework for Sequential Recommender Systems
Yang Sun, Fajie Yuan, Ming Yang, Guoao Wei, Zhou Zhao, and Duo Liu. 2020 · 2020
Closest in time.
DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2237–2240
Md Mehrab Tanjim, Hammad A Ayyubi, and Garrison W Cottrell. 2020 · 2020
Closest in time.
Learn-Prune-Share for Lifelong Learning
Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, and Stratis Ioannidis. 2020 · 2020
Closest in time.
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation. In Proceedings of The Web Conference 2020 . 303–313
Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. 2020a · 2020
Closest in time.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang. 2020b · 2020
Closest in time.
Amer: Automatic behavior modeling and interaction exploration in recommender system
Pengyu Zhao, Kecheng Xiao, Yuanxing Zhang, Kaigui Bian, and Wei Yan. 2020 · 2020
Closest in time.
A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models
Lei Chen, Fajie Yuan, Jiaxi Yang, Xiang Ao, Chengming Li, and Min Yang. 2021 · 2021
Closest in time.
Iterative Network Pruning with Uncertainty Regularization forLifelong Sentiment Classification. In Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval
Binzong Geng, Min Yang, Fajie Yuan, Shupeng Wang, Xiang Ao, and Ruifeng Xu. 2021 · 2021
Closest in time.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
Jiachun Wang, Fajie Yuan, Jian Chen, Qingyao Wu, Chengmin Li, Min Yang, Yang Sun, and Guoxiao Zhang. 2021 · 2021
Closest in time.