Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation-Reference-Cited by-同舟云学术

Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation

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

Li Haoxuan¹^ORCID,Zheng Chunyuan²^ORCID,Wu Peng³^ORCID,Kuang Kun⁴^ORCID,Liu Yue⁵^ORCID,Cui Peng⁶^ORCID

Affiliation:

1. Peking University, Beijing, China

2. University of California, San Diego, San Diego, USA

3. Beijing Technology and Business University, Beijing, China

4. Zhejiang University, Hangzhou, China

5. Renmin University of China, Beijing, China

6. Tsinghua University, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599550

Reference77 articles.

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3. Causal embeddings for recommendation

4. Chih-Yao Chang , Xing Tang , Bo-Wen Yuan , Jui-Yang Hsia , Zhirong Liu , Zhenhua Dong , Xiuqiang He , and Chih-Jen Lin . 2020. AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction . In MDM. IEEE , 202--209. Chih-Yao Chang, Xing Tang, Bo-Wen Yuan, Jui-Yang Hsia, Zhirong Liu, Zhenhua Dong, Xiuqiang He, and Chih-Jen Lin. 2020. AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction. In MDM. IEEE, 202--209.

5. Jiawei Chen Hande Dong Yang Qiu Xiangnan He Xin Xin Liang Chen Guli Lin and Keping Yang. 2021. AutoDebias: Learning to Debias for Recommendation. In SIGIR. Jiawei Chen Hande Dong Yang Qiu Xiangnan He Xin Xin Liang Chen Guli Lin and Keping Yang. 2021. AutoDebias: Learning to Debias for Recommendation. In SIGIR.

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