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Recently, deep learning models have been widely spread in the industrial recommender systems and boosted the recommendation quality.
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Neural collaborative filtering
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Deep & cross network for ad click predictions
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Attentional factorization machines: learning the weight of feature interactions via attention networks
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Recurrent neural networks with top-k gains for session-based recommendations
Balázs Hidasi and Alexandros Karatzoglou · 2018
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Squeeze-and-excitation networks
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley · 2018
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Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction
Tongwen Huang, Zhiqi Zhang, and Junlin Zhang · 2019
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Random search and reproducibility for neural architecture search
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xdeepfm: Combining explicit and implicit feature interactions for recommender systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Stamp: short-term attention/memory priority model for session-based recommendation
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Product-based neural networks for user response prediction over multi-field categorical data
Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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The evolved transformer
David So, Quoc Le, and Chen Liang · 2019
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Session-based recommendation with graph neural networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan · 2019
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Snas: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Graph contextualized self-attention network for session-based recommendation
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou · 2019
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A simple convolutional generative network for next item recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He · 2019
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Deep interest evolution network for click-through rate prediction
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai · 2019
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction
Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu · 2020
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Hugectr: a high-efficiency gpu framework for click-through-rate (ctr) estimation training
NVIDIA · 2020
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Merlin: A gpu accelerated recommendation framework
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2020
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Towards automated neural interaction discovery for click-through rate prediction
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Attentivenas: Improving neural architecture search via attentive sampling
Dilin Wang, Meng Li, Chengyue Gong, and Vikas Chandra · 2020
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Textnas: A neural architecture search space tailored for text representation
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Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2020
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How to train your super-net: An analysis of training heuristics in weight-sharing nas
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Preference-aware mask for session-based recommendation with bidirectional transformer
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