Multivariate Preference Models and Decision Making with the MAUT Machine
With the advent of e-commerce, systems supporting the user in finding just the right product in an electronic catalog have gained increasing attention. While collaborative recommender systems (RS) derive their suggestions from other users’ opinions, structure-based systems assess a product according to how well its properties satisfy a user’s preferences. This paper presents the MAUT Machine, a system implementing the basic machinery to be used by a structure-based RS to elicit and maintain complex user preference models and evaluate the entries of an electronic catalog according to their appropriateness for a given user or group of users.
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