AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems-Reference-Cited by-同舟云学术

AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
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Author:

Zhao Xiangyu¹,Liu Haochen²,Liu Hui²,Tang Jiliang²,Guo Weiwei³,Shi Jun³,Wang Sida³,Gao Huiji³,Long Bo⁴

Affiliation:

1. Michigan State University and City University of Hong Kong, United States

2. Michigan State University, USA

3. Linkedin Corporation, USA

4. JD.com, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3450124

Reference42 articles.

1. Recommendations in location-based social networks: a survey

2. Andrew Brock Theodore Lim James M Ritchie and Nick Weston. 2017. Smash: one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344(2017). Andrew Brock Theodore Lim James M Ritchie and Nick Weston. 2017. Smash: one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344(2017).

3. Ting Chen Lala Li and Yizhou Sun. 2019. Differentiable product quantization for end-to-end embedding compression. arXiv preprint arXiv:1908.09756(2019). Ting Chen Lala Li and Yizhou Sun. 2019. Differentiable product quantization for end-to-end embedding compression. arXiv preprint arXiv:1908.09756(2019).

4. Wide & Deep Learning for Recommender Systems

5. Deep Neural Networks for YouTube Recommendations

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