We propose a topic modeling approach to the prediction of preferences in
pairwise comparisons. We develop a new generative model for pairwise
comparisons that accounts for multiple shared latent rankings that are
prevalent in a population of users. This new model also captures inconsistent
user behavior in a natural way. We show how the estimation of latent rankings
in the new generative model can be formally reduced to the estimation of topics
in a statistically equivalent topic modeling problem. We leverage recent
advances in the topic modeling literature to develop an algorithm that can
learn shared latent rankings with provable consistency as well as sample and
computational complexity guarantees. We demonstrate that the new approach is
empirically competitive with the current state-of-the-art approaches in
predicting preferences on some semi-synthetic and real world datasets.

我们提出了一种基于主题建模的方法来预测配对比较中的偏好，该方法利用新的生成模型来捕获预测人群中多种共享潜在排名以及自然的不一致性，并将潜在排名的估计形式地归约为相当于统计模型中的话题建模问题，在此基础上利用相关领域的新进展开发了一个算法，该算法可以以可证明的一致性、样本和计算复杂度的保证学习共享的潜在排名，并在一些半合成和真实世界数据集上证明了与当前最先进方法在预测偏好方面的实证竞争力。