Capturing User Intent for Information Retrieval

We study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving retrieval performance and user satisfaction. In this proposed research, we improve retrieval performance and user satisfaction for information retrieval by building a user model to capture user intent dynamically through analyzing behavioral information from retrieved relevant documents, and by combining captured user intent with the elements of an information retrieval system. We use decision theoretic principles and bayesian networks for building this model. The novelties of our approach lie with the fine-grained representation of the model, the ability to learn user knowledge incrementally and dynamically, the integration of user intent and system elements for improving retrieval performance and the unified evaluation framework to assess the accuracy of user intent captured and effectiveness of our model.