Antithetic and Monte Carlo kernel estimators for partial rankings
Antithetic and Monte Carlo kernel estimators for partial rankings
In the modern age, rankings data are ubiquitous and they are useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, most rankings data encountered in the real world are incomplete, which prevent the direct application of existing modelling tools for complete rankings. Our …