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Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders : evidence from machine learning and self-reports

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Abstract
For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants’ self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.
Keywords
Indoor landmark, Salience dominance, Wayfinder familiarity, Machine-learned ranking, Model interpretability

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MLA
Zhou, Zhiyong, et al. “Varying Salience in Indoor Landmark Selection for Familiar and Unfamiliar Wayfinders : Evidence from Machine Learning and Self-Reports.” INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS, eScholarship (University of California), 2021, doi:10.25436/E24S34.
APA
Zhou, Z., Weibel, R., & Huang, H. (2021). Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders : evidence from machine learning and self-reports. INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS. Presented at the GIScience 2021, Online. https://doi.org/10.25436/E24S34
Chicago author-date
Zhou, Zhiyong, Robert Weibel, and Haosheng Huang. 2021. “Varying Salience in Indoor Landmark Selection for Familiar and Unfamiliar Wayfinders : Evidence from Machine Learning and Self-Reports.” In INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS. eScholarship (University of California). https://doi.org/10.25436/E24S34.
Chicago author-date (all authors)
Zhou, Zhiyong, Robert Weibel, and Haosheng Huang. 2021. “Varying Salience in Indoor Landmark Selection for Familiar and Unfamiliar Wayfinders : Evidence from Machine Learning and Self-Reports.” In INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS. eScholarship (University of California). doi:10.25436/E24S34.
Vancouver
1.
Zhou Z, Weibel R, Huang H. Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders : evidence from machine learning and self-reports. In: INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS. eScholarship (University of California); 2021.
IEEE
[1]
Z. Zhou, R. Weibel, and H. Huang, “Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders : evidence from machine learning and self-reports,” in INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS, Online, 2021.
@inproceedings{8743210,
  abstract     = {{For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants’ self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.}},
  articleno    = {{E24S34}},
  author       = {{Zhou, Zhiyong and Weibel, Robert and Huang, Haosheng}},
  booktitle    = {{INTERNATIONAL CONFERENCE ON GISCIENCE SHORT PAPER PROCEEDINGS}},
  issn         = {{2573-783X}},
  keywords     = {{Indoor landmark,Salience dominance,Wayfinder familiarity,Machine-learned ranking,Model interpretability}},
  language     = {{eng}},
  location     = {{Online}},
  pages        = {{6}},
  publisher    = {{eScholarship (University of California)}},
  title        = {{Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders : evidence from machine learning and self-reports}},
  url          = {{http://doi.org/10.25436/E24S34}},
  year         = {{2021}},
}

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