Any automated decision support software must tailor its actions or recommendations to the preferences of different users. Thus it requires some representation of user preferences as well as a means of eliciting or otherwise learning the preferences of the specific user on whose behalf it is acting. While additive preference models offer a compact representation of multiattribute utility functions, and ease of elicitation, they are often overly restrictive. The more flexible generalized additive independence (GAI) model maintains much of the intuitive nature of additive models, but comes at the cost of much more complex elicitation. In this article, we summarize the key contributions of our earlier paper (UAI 2005): (a) the first elaboration of the semantic foundations of GAI models that allows one to engage in preference elicitation using local queries over small subsets of attributes rather than global queries over full outcomes; and (b) specific procedures for Bayesian preference elicitation of the parameters of a GAI model using such local queries.