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

Abstract

Most learning-to-rank recommendation models assume users prefer interacted items more than non-interacted ones. All non-interacted items have the same chance to be selected as negative samples. However, the observed interaction data usually shows long-tailed distributions, making traditional recommendation models trained on such data tend to rank popular items higher than users’ true preference and degrading the performance of recommender systems. Some existing debias methods focus on eliminating such popularity bias, while simply removing all the popularity bias may diminish recommendation accuracy because popularity also contains some useful information implying item attractiveness, making it pivotal to handling popularity bias in proper ways. In this paper, popularity is recognized to have both positive and negative effects on recommendation. The positive effect of popularity should be amplified while the negative effect should be mitigated. Therefore, this paper proposes two novel schemes to distinguish the positive and negative impact of popularity in learning-to-rank models. First, we propose a popular-weighted sampler to sample non-interacted popular items from users’ collaborative items as negative samples with higher probabilities. We believe that if a popular item is collaboratively similar to a user but not interacted with by him, it is more likely to be a true negative sample for the user. We sample such items with higher probabilities to mitigate the harmful negative popularity bias. Second, popularity could be a significant indicator of items’ intrinsic quality. Hence, we design a popularity regularization scheme to capture item attractiveness, prompting cold-start users to prefer popular items. Finally, we conduct extensive experiments to evaluate the performance of the proposed schemes. Experimental results demonstrate that our approach can improve existing learning-to-rank models’ performance and outperform state-of-the-art debias methods.