The 8th EAI International Conference on Mobile Computing, Applications and Services

Research Article

Quantifying the Uncertainty of Next-Place Predictions

  • @INPROCEEDINGS{10.4108/eai.30-11-2016.2267147,
        author={Paul Baumann and Marc Langheinrich and Anind Dey and Silvia Santini},
        title={Quantifying the Uncertainty of Next-Place Predictions},
        proceedings={The 8th EAI International Conference on Mobile Computing, Applications and Services},
        keywords={human mobility prediction uncertainty quantification analysis of mobile phone data sets},
  • Paul Baumann
    Marc Langheinrich
    Anind Dey
    Silvia Santini
    Year: 2016
    Quantifying the Uncertainty of Next-Place Predictions
    DOI: 10.4108/eai.30-11-2016.2267147
Paul Baumann1, Marc Langheinrich2, Anind Dey3, Silvia Santini2,*
  • 1: TU Dresden
  • 2: Università della Svizzera Italiana (USI)
  • 3: Carnegie Mellon University
*Contact email:


Context-aware systems use predictions about a user's future state (e.g., next movements, actions, or needs) in order to seamlessly trigger the display of information or the execution of services. Predictions, however, always have an associated uncertainty that, when above a certain threshold, should prevent a system from taking action due to the risk of getting it wrong''. In this work, we present a context-dependentlevel of trust'' estimator that is able to determine whether a prediction should be trusted -- and thus used to trigger an action -- or not. Our estimator relies on ensemble learning to adapt across different users and application scenarios. We demonstrate its performance in the context of a popular problem -- next-place prediction -- and show how it outperforms existing approaches. We also report on the results of a survey that investigated user attitudes towards mobile-phone-based personal assistants and their ability to trigger actions in response to predictions. While users appreciated such assistants, they had substantially different tolerance thresholds with respect to prediction errors depending on the use case. This further motivates the need for a context-dependent level of trust estimator.