Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation

Author:

Luo Dan1ORCID,Zou Lixin2ORCID,Ai Qingyao3ORCID,Chen Zhiyu4ORCID,Li Chenliang2ORCID,Yin Dawei5ORCID,Davison Brian D.1ORCID

Affiliation:

1. Lehigh University, Bethlehem, PA, USA

2. Wuhan University, Wuhan, China

3. Tsinghua University, Beijing, China

4. Amazon.com, Inc., Seattle, WA, USA

5. Baidu Inc., Beijing, China

Publisher

ACM

Reference57 articles.

1. Qingyao Ai, Keping Bi, Jiafeng Guo, and W. Bruce Croft. 2018a. Learning a Deep Listwise Context Model for Ranking Refinement. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08--12, 2019.

2. Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W. Bruce Croft. 2018b. Unbiased Learning to Rank with Unbiased Propensity Estimation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08--12, 2019.

3. Unbiased Learning to Rank

4. Hard Negatives or False Negatives

5. Olivier Chapelle and Yi Chang. 2011. Yahoo! Learning to Rank Challenge Overview. In Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010.

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