  We consider learning problems of an intuitive and concise preference model,
called lexicographic preference lists (LP-lists). Given a set of examples that
are pairwise ordinal preferences over a universe of objects built of attributes
of discrete values, we want to learn (1) an optimal LP-list that decides the
maximum number of these examples, or (2) a near-optimal LP-list that decides as
many examples as it can. To this end, we introduce a dynamic programming based
algorithm and a genetic algorithm for these two learning problems,
respectively. Furthermore, we empirically demonstrate that the sub-optimal
models computed by the genetic algorithm very well approximate the de facto
optimal models computed by our dynamic programming based algorithm, and that
the genetic algorithm outperforms the baseline greedy heuristic with higher
accuracy predicting new preferences.
