This paper addresses a user model and dialogue state definition in spoken consulting dialogue systems that help users in making decision. When selecting from a set of alternatives, users have various decision criteria for making decision. Users often do not have a definite goal or criteria for selection, and thus they may find not only what kind of information the system can provide but their own preference or factors that they should emphasize. In this paper, we model such consulting dialogue as partially observable Markov decision process (POMDP). We then present an optimization of dialogue strategy to help users make better decisions.