Fairly Adaptive Negative Sampling for Recommendations-Reference-Cited by-同舟云学术

Fairly Adaptive Negative Sampling for Recommendations

Published:2023-04-30 Issue: Volume: Page:
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Container-title:Proceedings of the ACM Web Conference 2023
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Author:

Chen Xiao¹^ORCID,Fan Wenqi²^ORCID,Chen Jingfan¹^ORCID,Liu Haochen³^ORCID,Liu Zitao⁴^ORCID,Zhang Zhaoxiang⁵^ORCID,Li Qing²^ORCID

Affiliation:

1. The Hong Kong Polytechnic University, Hong Kong and Center for Artificial Intelligence and Robotics, HKISI_CAS, Hong Kong

2. The Hong Kong Polytechnic University, Hong Kong

3. Michigan State University, USA

4. Guangdong Institute of Smart Education, Jinan University, China

5. Institute of Automation, Chinese Academy of Sciences, China and Center for Artificial Intelligence and Robotics, HKISI_CAS, Hong Kong

Funder

The Hong Kong Research Grants Council

The InnoHK project

The Hong Kong Polytechnic University

NSFC

Key Laboratory of Smart Education of Guangdong Higher Education Institutes, Jinan University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583355

Reference52 articles.

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2. Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. In arXiv preprint arXiv:2010.03240. Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. In arXiv preprint arXiv:2010.03240.

3. Knowledge-enhanced Black-box Attacks for Recommendations

4. Ting Chen Yizhou Sun Yue Shi and Liangjie Hong. 2017. On sampling strategies for neural network-based collaborative filtering. In KDD. Ting Chen Yizhou Sun Yue Shi and Liangjie Hong. 2017. On sampling strategies for neural network-based collaborative filtering. In KDD.

5. Ashok Cutkosky and Harsh Mehta . 2020 . Momentum improves normalized sgd . In International conference on machine learning. PMLR, 2260–2268 . Ashok Cutkosky and Harsh Mehta. 2020. Momentum improves normalized sgd. In International conference on machine learning. PMLR, 2260–2268.

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