Generative user models for Adaptive Information Retrieval

For information retrieval (IR) tasks, user models are used to estimate user's true intention and demand. Unfortunately, most user models are constructed in a specialized form that is not applied to other systems or domains. This specialization makes difficulty in sharing user models as common resources for developing information retrieval systems and for researching cognitive characteristics in various users. In order to solve this problem, we need a general user modeling method. In this paper, a user model based on probabilistic framework is proposed. We call this model as generative user model. The generative user model represents user's mental depth by latent (hidden) variables. It also have visible variables that mean word set and qualifier of each word as a subjective probability distribution. The model can handle uncertainty of user's subjectivity by probabilistic framework. Recent statistical studies for such latent models give learning algorithm. Our generative user model can be constructed from dataset taken by information retrieval tasks. As an example, we also introduce two different kinds of information retrieval systems, ART MUSEUM (Multimedia Database with Sense of Color and Construction upon the Matter of ART) and DSIU (Decision Support for Internet Users). The generative user model is applied to these systems. The properties of the model and interactive learning mechanism are shown.