In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.

翻译：在现代推荐系统中，无偏学习排序对于从有偏隐式用户反馈（如点击数据）中对项目进行优先级排序至关重要。针对单边市场，已提出多种技术，例如逆概率加权。然而，双边市场（如求职平台或约会服务）中，成功转化需要同时匹配双方用户的偏好，但这一问题受到的关注较少。本文解决了双边市场中用户间偏差的复杂交互问题，并提出了一种定制化的学习排序方法。我们首先阐述了双边匹配平台中反馈机制的公式，并指出其隐式反馈可能包含来自两组用户的位置偏差。基于这一观察，我们扩展了逆概率加权估计器，并提出了一种名为双边逆概率加权的新估计器，以解决双边市场中的位置偏差。我们证明了所提估计器满足真实排序指标的无偏性。我们在真实双边平台上进行了数值实验，并从精度和鲁棒性两方面展示了所提方法的有效性。实验表明，我们的方法在处理训练数据中较少出现的稀有项目时，尤其优于基线方法。