Choice-based recommender systems
Paula Saavedra‐Nieves, Pilar Barreiro Elorza, Ricardo Duran, Rosa M. Crujeiras, María L. Loureiro, Eduardo Sánchez Vila · Conference on Recommender Systems · 2016
Choice-based models are proposed to overcome some of the limitations found in traditional rating-based strategies. The new approach is grounded on decision-making paradigms, such as choice and utility theories. Specifically, random utility models were applied in a recommendation problem. Prediction accuracy was compared with state-of-art rating-based algorithms in a gastronomy dataset. The results show the superior performance of choice-based models, which may suggest that real choices could bring more predictive power than ratings.