Learning and visualizing user preferences over sets

In previous work, we introduced a representation language,DD-PREF, that balances preferences for particular items with
preferences about the properties of the set. Specifically, DD-
PREF permits the expression of preferences for depth (i.e.,
preferences for specific attribute values over others) and di-
versity of sets (i.e., preferences for broad vs. narrow distri-
butions of attribute values). We have also shown how pref-
erences represented in DD-PREF can be learned from training
data. In this paper, we present a new experimental method-
ology, and give results in a Mars rover image domain. We
also provide new visualizations of the learned preferences in
this domain. Finally, we describe a Chinese restaurant menu
domain for which we are currently gathering user data.