Recursive partitioning: an approach based on the weighted kemeny distance

In the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures. The possibility to derive consensus measures using a classification tree represents a novelty and an important tool, given its easy interpretability. This work proposes the use of a univariate decision tree for ranking data based on the weighted Kemeny distance. The performance of the methodology will be shown by using a real dataset about university rankings.