AdaFS: Adaptive Feature Selection in Deep Recommender System-Reference-Cited by-同舟云学术

AdaFS: Adaptive Feature Selection in Deep Recommender System

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Lin Weilin¹,Zhao Xiangyu¹,Wang Yejing¹,Xu Tong²,Wu Xian³

Affiliation:

1. City University of Hong Kong, Hong Kong, Hong Kong

2. University of Science and Technology of China, Hefei, China

3. Tencent Jarvis Lab, Beijing, China

Funder

APRC - CityU New Research Initiatives

CCF-Tencent Open Fund

SIRG - CityU Strategic Interdisciplinary Research Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539204

Reference60 articles.

1. Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE TPAMI ( 2013 ). Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE TPAMI (2013).

2. Bo Chen , Xiangyu Zhao , Yejing Wang , Wenqi Fan , Huifeng Guo , and Ruiming Tang . 2022. Automated Machine Learning for Deep Recommender Systems: A Survey. arXiv preprint arXiv:2204.01390 ( 2022 ). Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, and Ruiming Tang. 2022. Automated Machine Learning for Deep Recommender Systems: A Survey. arXiv preprint arXiv:2204.01390 (2022).

3. Wide & Deep Learning for Recommender Systems

4. Attacking Black-box Recommendations via Copying Cross-domain User Profiles

5. AutoGFS: Automated Group-based Feature Selection via Interactive Reinforcement Learning

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