Constraint Satisfaction Methods for Information Personalization

Constraints formalize the dependencies in a physical world in terms of a logical relation among several unknowns. Constraint satisfaction methods allow efficient navigation of large search spaces to find an optimal solution that satisfies given constraints. This paper explores the application of constraint satisfaction methods to personalize generic information content with respect to a user-model. We present a constraint satisfaction based information personalization framework that (a) generates personalized information via the dynamic selection and synthesis of multiple information-snippets; and (b) ensures that the dynamically adapted personalized information is factually consistent. We present four constraint satisfaction methods that cumulatively work to maximize collaboration and minimize conflicts between a set of information-snippets in order to dynamically generate personalized information.