Towards Unbiased and Robust Causal Ranking for Recommender Systems

Author:

Xiao Teng1,Wang Suhang1

Affiliation:

1. Pennsylvania State University, State College, PA, USA

Funder

National Science Foundation

Army Research Office

Publisher

ACM

Reference54 articles.

1. The im algorithm: a variational approach to information maximization;Felix Agakov David Barber;NIPS,2004

2. Aman Agarwal Kenta Takatsu Ivan Zaitsev and Thorsten Joachims. 2019. A general framework for counterfactual learning-to-rank. In SIGIR . 5--14. Aman Agarwal Kenta Takatsu Ivan Zaitsev and Thorsten Joachims. 2019. A general framework for counterfactual learning-to-rank. In SIGIR . 5--14.

3. Alexander A Alemi , Ian Fischer , Joshua V Dillon , and Kevin Murphy . 2017 . Deep variational information bottleneck . International Conference on Learning Representations (2017). Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2017. Deep variational information bottleneck. International Conference on Learning Representations (2017).

4. Mohammad Taha Bahadori , Krzysztof Chalupka , Edward Choi , Robert Chen , Walter F Stewart , and Jimeng Sun . 2017. Causal regularization. arXiv preprint arXiv:1702.02604 ( 2017 ). Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F Stewart, and Jimeng Sun. 2017. Causal regularization. arXiv preprint arXiv:1702.02604 (2017).

5. Doubly Robust Estimation in Missing Data and Causal Inference Models

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