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User Modeling and User-Adapted Interaction

, Volume 4, Issue 2, pp 107–130 | Cite as

Heterogeneous learning in the Doppelgänger user modeling system

  • Jon Orwant
Article

Abstract

Doppelgänger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications.Doppelgänger's learning is calledheterogeneous 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 onDoppelgänger'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 

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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Jon Orwant
    • 1
  1. 1.The Media LaboratoryMITCambridgeUSA

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