Mapping Preferences into Euclidean Space Oscar Luaces a , Jorge D´ ıez a,* , Thorsten Joachims b , Antonio Bahamonde a,b a Universidad de Oviedo, Artificial Intelligence Center, Gij´ on, Asturias, Spain b Cornell University, Department of Computer Science, Ithaca, NY, USA Abstract Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to pre- dicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products. Notice that consumers’ clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about con- sumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588 Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge D´ ıez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with Applications July 15, 2015