User Modeling via Stereotypes*

TLDR

User modeling must account for individual personalities, goals, and inference conflicts, requiring accurate stereotypes for systems like Grundy. The paper proposes using stereotypes to build individual user models from limited information. Grundy builds and refines user models using stereotypes that incorporate uncertain knowledge, then uses these models to recommend novels. Analysis shows Grundy's stereotype‑based user models effectively guide its novel recommendations.

Abstract

This paper addresses the problems that must be considered if computers are going to treat their users as individuals with distinct personalities, goals, and so forth. It first outlines the issues, and then proposes stereotypes as a useful mechanism for building models of individual users on the basis of a small amount of information about them. In order to build user models quickly, a large amount of uncertain knowledge must be incorporated into the models. The issue of how to resolve the conflicts; hot will arise along such inferences is discussed. A system, Grundy, is described that builds models of its users, with the aid of stereotypes, and then exploits those models to guide it in its task, suggesting novels that people may find interesting. If stereotypes are to be useful to Grundy, they must accurately characterize the users of the system. Some techniques to modify stereotypes on the basis of experience are discussed. An analysis of Grundy's performance shows that its user models are effective in guiding its performance.