Learning Optimal and Near-Optimal Lexicographic Preference Lists
This addresses preference modeling for decision-making systems, but it is incremental as it builds on existing LP-list concepts with new algorithms.
The paper tackled learning lexicographic preference lists (LP-lists) from pairwise ordinal preferences, introducing a dynamic programming algorithm for optimal solutions and a genetic algorithm for near-optimal ones. The result showed that the genetic algorithm approximates optimal models well and outperforms a greedy baseline with higher accuracy in predicting new preferences.
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.