Relational networks of conditional preferences

摘要

Much like relational probabilistic models, the need for relational preference models naturally arises in real-world applications involving multiple, heterogeneous, and richly interconnected objects. On the one hand, relational preferences should be represented into statements which are natural for human users to express. On the other hand, relational preference models should be endowed with a structure that supports tractable forms of reasoning and learning. Based on these criteria, this paper introduces the framework of relational conditional preference networks (RCP-nets), that maintains the spirit of the popular “CP-nets” by expressing relational preferences in a natural way using the ceteris paribus semantics. We show that acyclic RCP-nets support tractable inference for optimization and ranking tasks. In addition, we show that in the online learning model, tree-structured RCP-nets (with bipartite orderings) are efficiently learnable from both optimization tasks and ranking tasks, using linear loss functions. Our results are corroborated by experiments on a large-scale movie recommendation dataset.