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A System for Context-Dependent User Modeling

  • Petteri Nurmi
  • Alfons Salden
  • Sian Lun Lau
  • Jukka Suomela
  • Michael Sutterer
  • Jean Millerat
  • Miquel Martin
  • Eemil Lagerspetz
  • Remco Poortinga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4278)

Abstract

We present a system for learning and utilizing context-dependent user models. The user models attempt to capture the interests of a user and link the interests to the situation of the user. The models are used for making recommendations to applications and services on what might interest the user in her current situation. In the design process we have analyzed several mock-ups of new mobile, context-aware services and applications. The mock-ups spanned rather diverse domains, which helped us to ensure that the system is applicable to a wide range of tasks, such as modality recommendations (e.g., switching to speech output when driving a car), service category recommendations (e.g., journey planners at a bus stop), and recommendations of group members (e.g., people with whom to share a car). The structure of the presented system is highly modular. First of all, this ensures that the algorithms that are used to build the user models can be easily replaced. Secondly, the modularity makes it easier to evaluate how well different algorithms perform in different domains. The current implementation of the system supports rule based reasoning and tree augmented naïve Bayesian classifiers (TAN). The system consists of three components, each of which has been implemented as a web service. The entire system has been deployed and is in use in the EU IST project MobiLife. In this paper, we detail the components that are part of the system and introduce the interactions between the components. In addition, we briefly discuss the quality of the recommendations that our system produces.

Keywords

Pervasive Computing Context Parameter Usage Record Context Provider Modality Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Petteri Nurmi
    • 1
  • Alfons Salden
    • 2
  • Sian Lun Lau
    • 3
  • Jukka Suomela
    • 1
  • Michael Sutterer
    • 3
  • Jean Millerat
    • 4
  • Miquel Martin
    • 5
  • Eemil Lagerspetz
    • 1
  • Remco Poortinga
    • 2
  1. 1.Helsinki Institute for Information Technology HIITUniversity of HelsinkiFinland
  2. 2.Telematica Instituut (TELIN)EnschedeThe Netherlands
  3. 3.Faculty of Electrical EngineeringUniversity of KasselKasselGermany
  4. 4.Motorola Labs, Parc Les Algorithmes, Saint-AubinGif-sur-Yvette CedexFrance
  5. 5.NEC Europe LtdHeidelbergGermany

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