Learning partially ranked data based on graph regularization
This addresses the problem of handling missing data in rankings for applications like voting and surveys, but it is incremental as it builds on existing methods with specific modifications.
The paper tackles parameter estimation for partially ranked data under non-ignorable missing mechanisms, proposing estimators that combine graph regularization with the EM algorithm and demonstrating their effectiveness in experiments.
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.