The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users

TL;DR

The paper presents Bayesian user modeling techniques to infer user goals and needs in software applications, enabling intelligent assistance and personalized interfaces, exemplified by the Office Assistant in Microsoft Office '97.

Contribution

It introduces novel Bayesian models for real-time inference of user goals, expertise, and needs from software interaction data, advancing intelligent user interface design.

Findings

Effective Bayesian models for user goal inference

Successful implementation in Microsoft Office '97 Office Assistant

Demonstrated ability to adapt to user expertise changes

Abstract

The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be employed to infer a users needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for the Office Assistant in the Microsoft Office '97 suite of productivity applications.