Modelling unobserved heterogeneity of ranking data with the Bayesian mixture of Extended Plackett-Luce models

The Plackett-Luce distribution (PL) is one of the most successful parametric options within the class of multistage ranking models to learn the preferences on a given set of items from a sample of ordered sequences. It postulates that the ranking process is carried out by sequentially assigning the positions according to the forward order, that is, from the top (most-liked) to the bottom (least-liked) alternative. This assumption has been relaxed with the Extended Plackett-Luce model (EPL), thanks to the introduction of the reference order parameter describing the rank attribution path. Starting from the recent formulation of the Bayesian EPL, in this work we investigate the further extension into the finite mixture approach as a method to explore the group structure of ranking data.