User modeling in intelligent information retrieval
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Empirical evaluation of adaptive user modeling in a medical information retrieval application
UM'03 Proceedings of the 9th international conference on User modeling
Sparse hidden-dynamics conditional random fields for user intent understanding
Proceedings of the 20th international conference on World wide web
Intelligent Decision Technologies - Special issue on Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments
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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.