Coarse preferences: representation, elicitation, and decision making
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Date
01/07/2019Author
Andreadis, Pavlos
Metadata
Abstract
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.