Causal Representation Learning for Out-of-Distribution Recommendation-Reference-Cited by-同舟云学术

Causal Representation Learning for Out-of-Distribution Recommendation

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Wang Wenjie¹,Lin Xinyu¹,Feng Fuli²,He Xiangnan²,Lin Min³,Chua Tat-Seng¹

Affiliation:

1. National University of Singapore, Singapore

2. University of Science and Technology of China, China

3. Sea AI Lab, Singapore

Funder

the National Natural Science Foundation of China

the Sea-NExT Joint Lab

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512251

Reference58 articles.

1. Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2020. The Connection Between Popularity Bias Calibration and Fairness in Recommendation. In RecSys. ACM 726–731. Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2020. The Connection Between Popularity Bias Calibration and Fairness in Recommendation. In RecSys. ACM 726–731.

2. Some new estimates of the ‘Jensen gap’

3. Yoshua Bengio Tristan Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle Olexa Bilaniuk Anirudh Goyal and Christopher J. Pal. 2020. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. In ICLR. Yoshua Bengio Tristan Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle Olexa Bilaniuk Anirudh Goyal and Christopher J. Pal. 2020. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. In ICLR.

4. Stephen Bonner and Flavian Vasile. 2018. Causal Embeddings for Recommendation. In RecSys. ACM 104–112. Stephen Bonner and Flavian Vasile. 2018. Causal Embeddings for Recommendation. In RecSys. ACM 104–112.

5. Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In RecSys. ACM 224–232. Allison JB Chaney Brandon M Stewart and Barbara E Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In RecSys. ACM 224–232.

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