Modelling and learning user preferences over sets

Although there has been significant research on modeling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e., preferences for specific attribute values over others) and on the diversity of sets (i.e., preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional objectand set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences; a real-world music playlist collection; and rover image data gathered in desert training exercises.