标题： 基于图正则化的部分排名数据学习 显示英文标题

标题： Learning partially ranked data based on graph regularization

摘要： 排名数据出现在许多不同的应用中，包括投票和消费者调查。往往会出现数据部分排序的情况。部分排序的数据被视为缺失数据。本文研究了在（可能的）非忽略缺失机制下部分排序数据的参数估计问题。我们提出了完整的排名估计量和缺失机制估计量，并提供了一个简单的估计程序。我们的估计程序结合了图正则化和期望最大化算法。理论上保证了我们的估计程序具有收敛性。通过允许非忽略的缺失机制，我们减少了模型偏差。此外，通过引入图正则化，我们避免了非忽略缺失机制中的固有复杂性。实验结果表明，所提出的估计器在非忽略缺失机制下表现良好。 显示英文摘要

摘要： Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing mechanism. We propose estimators for both complete rankings and missing mechanisms together with a simple estimation procedure. Our estimation procedure leverages a graph regularization in conjunction with the Expectation-Maximization algorithm. Our estimation procedure is theoretically guaranteed to have the convergence properties. We reduce a modeling bias by allowing a non-ignorable missing mechanism. In addition, we avoid the inherent complexity within a non-ignorable missing mechanism by introducing a graph regularization. The experimental results demonstrate that the proposed estimators work well under non-ignorable missing mechanisms.