标题： 从多体比较中高效推断排名 显示英文标题

标题： Efficient inference of rankings from multi-body comparisons

摘要： 许多现有的方法在评估和预测竞争比赛中选手、队伍或产品的表现时，都假设比较是在两个实体之间进行的。然而，也有一些真实的比赛，其中每次比较涉及超过两个实体，例如体育赛事、多人棋盘和卡牌游戏以及偏好调查。Plackett-Luce（PL）模型为推断此类由多体比较特征的比赛中涉及的实体排名提供了一种合理的方法。不幸的是，传统用于计算PL排名的算法收敛速度较慢，限制了PL模型在相对小规模系统中的应用。我们在这里提出了一种替代实现方式，可以显著提高速度，并在合成和真实世界的数据集中验证了其效率。此外，我们还进行了系统性的交叉验证测试，以评估PL模型预测未观察到比较的能力。我们发现，基于包含多体比较的训练集训练的PL模型比基于从同一训练集派生的投影成对比较的PL模型更具预测性，这强调了在有相关信息时，正确考虑现实世界系统真正多体性质的必要性。 显示英文摘要

摘要： Many of the existing approaches to assess and predict the performance of players, teams or products in competitive contests rely on the assumption that comparisons occur between pairs of such entities. There are, however, several real contests where more than two entities are part of each comparison, e.g., sports tournaments,multiplayer board and card games, and preference surveys. The Plackett-Luce (PL) model provides a principled approach to infer the ranking of entities involved in such contests characterized by multi-body comparisons. Unfortunately, traditional algorithms used to compute PL rankings suffer from slow convergence limiting the application of the PL model to relatively small-scale systems. We present here an alternative implementation that allows for significant speed-ups and validate its efficiency in both synthetic and real-world sets of data. Further, we perform systematic cross-validation tests concerning the ability of the PL model to predict unobserved comparisons. We find that a PL model trained on a set composed of multi-body comparisons is more predictive than a PL model trained on a set of projected pairwise comparisons derived from the very same training set, emphasizing the need of properly accounting for the true multi-body nature of real-world systems whenever such an information is available.