Bayesian Aggregation of Order-Based Rank Data
基于排序数据的贝叶斯聚合AI 解读 ↓
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基于排序数据的贝叶斯聚合
- 研究问题
- 当多个基础排序器的可靠性不同且信息可能重复时，如何合成排序，并避免等权或预设权重造成的信息利用不足？
- 方法与思路
- 作者提出 BARD，为各排序器设置质量参数，在聚合过程中共同估计可靠性，并识别高度相关排序器以处理信息冗余。
- 材料陈述的贡献
- 摘要称该方法可量化排序器可靠性；模拟与真实应用显示，在可靠性差异较大的情形下，其表现优于所比较的已有方法。
- 阅读关注点
- 应核对质量参数的可识别性、先验与计算方法、相关排序器的检测标准，以及模拟和应用中的评价指标与比较基准。
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原文摘要
Rank aggregation, that is, combining several ranking functions (called base rankers) to get aggregated, usually stronger rankings of a given set of items, is encountered in many disciplines. Most methods in the literature assume that base rankers of interest are equally reliable. It is very common in practice, however, that some rankers are more informative and reliable than others. It is desirable to distinguish high quality base rankers from low quality ones and treat them differently. Some methods achieve this by assigning prespecified weights to base rankers. But there are no systematic and principled strategies for designing a proper weighting scheme for a practical problem. In this article, we propose a Bayesian approach, called Bayesian aggregation of rank data (BARD), to overcome this limitation. By attaching a quality parameter to each base ranker and estimating these parameters along with the aggregation process, BARD measures reliabilities of base rankers in a quantitative way and makes use of this information to improve the aggregated ranking. In addition, we design a method to detect highly correlated rankers and to account for their information redundancy appropriately. Both simulation studies and real data applications show that BARD significantly outperforms existing methods when equality of base rankers varies greatly.