Model-Based Learning from Preference Data

Qinghua Liu, Marta Crispino, Ida Scheel, Valeria Vitelli, Arnoldo Frigessi
2018 Annual Review of Statistics and Its Application  
Preference data occurs when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking or clicking. The purpose of preference learning is to (i) infer on the shared consensus preference of a group of users, sometimes called rank aggregation; or (ii) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; this is an important part of recommender systems. We provide an overview of probabilistic
more » ... proaches to preference learning, including the Mallows, Plackett-Luce, Bradley-Terry models and collaborative filtering, and some of their variations. We illustrate, compare and discuss the use of these models by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this paper is not to recommend the use of one best method, but to present a palette of different possibilities for different questions and different types of data.
doi:10.1146/annurev-statistics-031017-100213 fatcat:vtykf5bp5zconbvy6mvwktqtby