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The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors.
Autoemb: Automated embedding dimensionality search in streaming recommendations
Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang · 2002
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Memory-efficient embedding for recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long · 2006
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
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Steffen Rendle · 2010
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Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares
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Tensorflow: A system for large-scale machine learning
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Wide & deep learning for recommender systems
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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Mixed dimension embeddings with application to memory-efficient recommendation systems
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Automated machine learning: methods, systems, challenges
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Differentiable neural input search for recommender systems
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Neural input search for large scale recommendation models
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Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, and Ed H Chi · 2020
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Soft threshold weight reparameterization for learnable sparsity
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Pytorch: An imperative style, high-performance deep learning library
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