  Various tasks in decision making and decision support systems require
selecting a preferred subset of a given set of items. Here we focus on problems
where the individual items are described using a set of characterizing
attributes, and a generic preference specification is required, that is, a
specification that can work with an arbitrary set of items. For example,
preferences over the content of an online newspaper should have this form: At
each viewing, the newspaper contains a subset of the set of articles currently
available. Our preference specification over this subset should be provided
offline, but we should be able to use it to select a subset of any currently
available set of articles, e.g., based on their tags. We present a general
approach for lifting formalisms for specifying preferences over objects with
multiple attributes into ones that specify preferences over subsets of such
objects. We also show how we can compute an optimal subset given such a
specification in a relatively efficient manner. We provide an empirical
evaluation of the approach as well as some worst-case complexity results.
