In this paper, we report our development of a hybrid user model for improving a user's effectiveness in a search. Specifically, we dynamically capture a user's intent and combine the captured user intent with the elements of an information retrieval system in a decision theoretic framework. Our solution is to identify a set of key attributes describing a user's intent, and determine the interactions among them. Then we build our user model by capturing these attributes, which we call the IPC model. We further extend this model to combine the captured user intent with the elements of an information retrieval system in a decision theoretic framework, thus creating a hybrid user model. In this hybrid user model, we use multi-attribute utility theory. We take advantage of the existing research on predicting query performance and on determining dissemination thresholds to create the functions to evaluate these chosen attributes. The main contribution of this research lies with the integration of user intent and system elements in a decision theoretic framework. Our approach also offers fine-grained representation of the model and the ability to learn a user's knowledge dynamically over time. We compare our approach with the best traditional approach in the information retrieval community - Ide dec-hi using term frequency inverted document frequency weighting on selected collections from the information retrieval community such as CRANFIELD, MEDLINE, and CACM.