Creating personalized systems that people can scrutinize and control: Drivers, principles and experience

  • Authors:

  • Judy Kay;Bob Kummerfeld

  • Affiliations:

  • University of Sydney, Australia;University of Sydney, Australia

  • Venue:
  • ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
  • Year:
  • 2013

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Abstract

Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems. This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.