Relational Networks of Conditional Preferences
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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. This paper introduces the framework of conditional preference relational networks (CPR-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 CPR-nets support tractable inference for optimization and ranking tasks. In addition, we show that in the online learning model, tree-structured CPR-nets are efficiently learnable from both optimization tasks and ranking tasks. Our results are corroborated with experiments on a large-scale movie recommendation dataset.
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