Mapping preferences into Euclidean space

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 predicting 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 consumers 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 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

Application of Deep Learning in Food: A Review

“…Some researchers tried to find the relationship between food sensory parameters and catering hedonic data using traditional data processing methods such as PLSR, SVM, neural networks, and so on. The mentioned algorithms were employed as the data mining tools to build up the linear or nonlinear model to estimate hedonic data using food sensory parameters (Luaces, Díez, Joachims, & Bahamonde, ). The sensory characteristics of food itself have a great impact on dietary consumption.…”

A peer assessment method to provide feedback, consistent grading and reduce students' burden in massive teaching settings

“…There are many reasons in favor of the ordinal approach, not only in assessment, but in general when we are interested in learning preferences in contexts like information retrieval or marketing studies (Bahamonde et al, 2004;Joachims, 2002;Luaces et al, 2015c).…”

“…As mentioned above, the first step toward an assessment is to fill the matrix according to the available values. For this purpose, we start out from a set of preference judgments (Bahamonde et al, 2004;Joachims, 2002;Luaces et al, 2015c),…”

Optimizing novelty and diversity in recommendations

“…Finally, let us remark that if we have any extra information about readers or news, we just have to concatenate the vectorial representation described above with a vectorial representation of extra knowledge. This idea has been successfully used in [19,18]. For instance, we could have some valuable information about readers, like sex, age, previous interactions with the digital newspaper, etc... On the other hand, the news could have been described by using their contents.…”