Heterogeneous Learning in the Doppelgänger User Modeling System (1995)
| Venue: | Interaction |
| Citations: | 64 - 0 self |
BibTeX
@ARTICLE{Orwant95heterogeneouslearning,
author = {Jon Orwant},
title = {Heterogeneous Learning in the Doppelgänger User Modeling System},
journal = {Interaction},
year = {1995},
volume = {4},
pages = {107--130}
}
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Abstract
Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on Doppelg anger's learning techniques and their implementation in an application-independent, sensor-independent environment. Key words: User model, machine learning, server-client architecture, multivariate statistical analysis, Markov models, Beta distribution, linear prediction. 1 Introduction When users interact with a computer, they provide a great deal of information about themselves. Even when they are not physically at a computer console,...
