Learning-to-rank debias with popularity-weighted negative sampling and popularity regularization

Chenxu Wang, Aodian Liu, Tao Qin

Abstract: Highlights•We design a novel popularity-weighted sampler to sample negative items.•We propose a popularity regularization term to capture items' attractiveness.•We evaluate the performance of the schemes with extensive experiments.