Utility Estimation in Large Preference Graphs Using A* Search

Existing preference prediction techniques can require that an entire preference structure be constructed for a user. These structures, such as Conditional Outcome Preference Networks (COP-nets), can grow exponentially in the number of attributes describing the outcomes. In this paper, a new approach for constructing COP-nets, using A* search, is introduced. Using this approach, partial COP-nets can be constructed on demand instead of generating the entire structure. Experimental results show that the new method yields enormous savings in time and memory requirements, with only a modest reduction in prediction accuracy.