Coarse preferences: representation, elicitation, and decision making
In this thesis we present a theory for learning and inference of user preferences with a novel hierarchical representation that captures preferential indifference. Such models of ’Coarse Preferences’ represent the space of solutions with a uni-dimensional, discrete latent space of ’categories’. This results in a partitioning of the space of solutions into preferential equivalence classes. This hierarchical model significantly reduces the computational burden of learning and inference, with improvements both in computation time and convergence behaviour with respect to number of samples. We argue that this Coarse Preferences model facilitates the efficient solution of previously computationally prohibitive recommendation procedures. The new problem of ’coordination through set recommendation’ is one such procedure where we formulate an optimisation problem by leveraging the factored nature of our representation. Furthermore, we show how an on-line learning algorithm can be used for the efficient solution of this problem. Other benefits of our proposed model include increased quality of recommendations in Recommender Systems applications, in domains where users’ behaviour is consistent with such a hierarchical preference structure. We evaluate the usefulness of our proposed model and algorithms through experiments with two recommendation domains - a clothing retailer’s online interface, and a popular movie database. Our experimental results demonstrate computational gains over state of the art methods that use an additive decomposition of preferences in on-line active learning for recommendation.