从比赛结果推断选手实力，自动学习胜负概率关系
Model inference for ranking from pairwise comparisons
- 联合推断选手隐含实力与胜负概率映射函数
- 在真实数据集上验证方法对模型设定不敏感
- 适合无先验知识的排名场景，如体育赛事分析
我们研究从噪声配对比较中排序对象的问题，例如根据比赛结果排名网球选手。采用标准方法，假设每个对象具有未观测到的强度，且每次比较结果由参与对象强度的概率决定。但不预先假设强度如何影响结果。为此，提出一种高效算法，同时推断未观测强度和将强度映射为概率的函数。尽管该问题欠约束，实验表明贝叶斯方法结论对不同模型设定具有鲁棒性。通过多个案例研究，展示了该方法在真实数据集上的应用效果。
原文摘要 · Abstract (English)
We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this problem and assume that each object has an unobserved strength and that the outcome of each comparison depends probabilistically on the strengths of the comparands. However, we do not assume to know a priori how skills affect outcomes. Instead, we present an efficient algorithm for simultaneously inferring both the unobserved strengths and the function that maps strengths to probabilities. Despite this problem being under-constrained, we present experimental evidence that the conclusions of our Bayesian approach are robust to different model specifications. We include several case studies to exemplify the method on real-world data sets.
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