Learning Binary Preference Relations

It is a truth universally acknowledged that e-commerce platform users in search of an item that best suits their preferences may be o‚ered a lot of choices. An item may be characterised by many aŠributes, which can complicate the process. Here the classic approach in decision support systems to put weights on the importance of each aŠribute is not always helpful as users may €nd it hard to formulate their priorities explicitly. Pairwise comparisons provide an easy way to elicit the user’s preferences in the form of the simplest possible qualitative preferences, which can then be combined to rank the available alternatives. We focus on this type of preference elicitation and learn the individual preference by applying one statistical approach based on Support Vector Machines (SVM), and two logic-based approaches: Inductive Logic Programming (ILP) and Decision Trees. All approaches are compared on two datasets of car preferences and sushi preferences collected from human participants. While in general, the statistical approach has proven its practical advantages, our experiment shows that the logic-based approaches o‚er a number of bene€ts over the one based on statistics. CCS CONCEPTS •Information systems →Recommender systems;