具有对象特征的无监督秩聚集的单调重定向
Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
摘要：
通过聚合一组专家意见排序列表来学习对象之间的真实排序是从社会选择理论到自然语言处理和搜索聚合的许多应用中的重要且普遍存在的问题。我们研究了无监督等级聚合的问题，其中没有地面真理订购信息可用，无论是关于任何一组对象之间的真实偏好排序，也没有关于个人排名列表的质量。以这种无监督的方式聚合经常不一致和质量差的排名列表是一个极具挑战性的问题，并且标准的基于共识的方法通常定义不清，难以解决。在这篇手稿中，我们提出了一个新的框架来绕过这些问题，使用对象属性来增强标准的排名聚合框架。我们设计的算法，学习联合模型的排名列表和对象的功能，以获得一个聚合的排名排序，是更准确和强大的，也有助于淘汰排名列表的可疑的有效性。我们验证我们的技术在合成数据集，我们的算法是能够估计真正的排名排序，即使当排名列表被损坏。在MQ2008、MQ2008和OHSUMED三个真实数据集上的实验表明，与现有的不使用对象信息的秩聚合方法相比，使用对象特征可以显著提高性能。此外，当至少有一些排名列表是高质量的，我们的方法能够有效地利用他们的高专业知识，输出一个聚合的排名排序的高精度。
英文摘要：
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground truth ordering information in available, neither about the true preference ordering between any set of objects nor about the quality of individual rank lists. Aggregating the often inconsistent and poor quality rank lists in such an unsupervised manner is a highly challenging problem, and standard consensus-based methods are often ill-defined, and difficult to solve. In this manuscript we propose a novel framework to bypass these issues by using object attributes to augment the standard rank aggregation framework. We design algorithms that learn joint models on both rank lists and object features to obtain an aggregated rank ordering that is more accurate and robust, and also helps weed out rank lists of dubious validity. We validate our techniques on synthetic datasets where our algorithm is able to estimate the true rank ordering even when the rank lists are corrupted. Experiments on three real datasets, MQ2008, MQ2008 and OHSUMED, show that using object features can result in significant improvement in performance over existing rank aggregation methods that do not use object information. Furthermore, when at least some of the rank lists are of high quality, our methods are able to effectively exploit their high expertise to output an aggregated rank ordering of great accuracy.
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