Counterfactual Video Recommendation for Duration Debiasing-Reference-Cited by-同舟云学术

Counterfactual Video Recommendation for Duration Debiasing

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Tang Shisong¹^ORCID,Li Qing²^ORCID,Wang Dingmin³^ORCID,Gao Ci⁴^ORCID,Xiao Wentao¹^ORCID,Zhao Dan⁵^ORCID,Jiang Yong¹^ORCID,Ma Qian⁶^ORCID,Zhang Aoyang⁶^ORCID

Affiliation:

1. Tsinghua University, Beijing, China

2. Peng Cheng Laboratory, Shenzhen, China

3. University of Oxford, Oxford, United Kingdom

4. Jilin university, Changchun, China

5. Peng Cheng Laboratory, Beijing, China

6. ByteDance Inc., Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Shenzhen Key Lab of Software Defined Networking

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599797

Reference42 articles.

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2. Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

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4. Deep Neural Networks for YouTube Recommendations

5. Jingtao Ding Yuhan Quan Xiangnan He Yong Li and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data.. In IJCAI. 2230--2236. Jingtao Ding Yuhan Quan Xiangnan He Yong Li and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data.. In IJCAI. 2230--2236.

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