Generative user models for adaptive information retrieval

Abstract

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 it difficult to share 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. A user model based on a probabilistic framework is proposed. We call this model a generative user model. The generative user model represents user's mental depth by latent (hidden) variables. It also has visible variables that mean word set and qualifier of each word as a subjective probability distribution. The model can handle uncertainty of the user's subjectivity by a probabilistic framework. Recent statistical studies for such latent models give a learning algorithm. Our generative user model can be constructed from a 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.