Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
Author:
Affiliation:
1. School of Software, Shandong University, Jinan, China
2. Alibaba Group, Beijing, China
3. Alibaba Group, Hangzhou, China
Funder
Taishan Scholar Program of Shandong Province in China
National Natural Science Foundation of China
National Key Research and Development Program of China
Alibaba Innovation Research Program
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3616855.3635810
Reference61 articles.
1. Qingyao Ai Keping Bi Jiafeng Guo and W Bruce Croft. 2018. Learning a deep listwise context model for ranking refinement. In SIGIR. 135--144.
2. Qingyao Ai Keping Bi Cheng Luo Jiafeng Guo and W Bruce Croft. 2018. Unbiased learning to rank with unbiased propensity estimation. In SIGIR. 385--394.
3. Qingyao Ai Xuanhui Wang Sebastian Bruch Nadav Golbandi Michael Bendersky and Marc Najork. 2019. Learning groupwise multivariate scoring functions using deep neural networks. In SIGIR. 85--92.
4. Qingyao Ai Tao Yang Huazheng Wang and Jiaxin Mao. [n. d.]. Unbiased learning to rank: online or offline? TOIS ([n. d.]).
5. Aijun Bai Rolf Jagerman Zhen Qin Le Yan Pratyush Kar Bing-Rong Lin Xuanhui Wang Michael Bendersky and Marc Najork. 2023. Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance. In CIKM. 4502--4508.
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