Discriminant Analysis as a Machine Learning Method for Revision of User Stereotypes of Information Retrieval Systems

This paper proposes to use the discriminant analysis technique as a machine learning method to adjust memberships of stereotypes, based on the user’s in-depth, task-related knowledge contained in the user models. The paper reports an empirical study on the user stereotypes of information retrieval (IR) systems. The participants were first assigned into stereotypes based on their self-reported characteristics. Their memberships in the stereotypes were then tested and predicted using the discriminant analysis, based on their IR knowledge. The pre-assigned membership and the predicted membership of each stereotype were compared. The study demonstrates that the discriminant analysis technique can be used to detect the conflicts between individual users’ knowledge and the assumption held by stereotypes that all members in a stereotype share common knowledge. The technique can be used to revise/reclassify a person’s membership of a stereotype based on the person’s knowledge. Implications and future directions of the study are discussed.