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Personality Based Data-Driven Personalization as an Integral Part of the Mobile Application

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Business Information Systems Workshops (BIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 444))

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Abstract

The article presents the results of the work on the method of intuitive UI and UX personalization of mobile applications. The method is based on the user’s personality profile (Big 5) inferred from the available data on the user’s phone at the time of installation. The user’s personality model was created based on machine learning performed on data from 2,202 people. The proposed method enables personalization from the first contact of the customer with the application. Therefore, it is a significant advantage of the study. Moreover, the method ensures complete data privacy protection since no data about the user is uploaded outside the mobile phone.

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Notes

  1. 1.

    The 25 items tool was created. Reliability, N = 3331: Alfa-Cronbach coefficient: E:.76, A:.58, O:.59, S:.72, C:.64). Accuracy: r-Pearson coefficients with IPIP-BFM-50: E:.85, A:.55, O:.62, S:.81, C:.76).

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Author information

Authors and Affiliations

  1. Poznan University of Economics and Business, al. Niepodległosci 10, 61-875, Poznan, Poland

    Izabella Krzeminska & Marcin Szmydt

  2. Orange Polska, R&D Labs, Obrzezna 7, Warszawa, Poland

    Izabella Krzeminska

Authors
  1. Izabella Krzeminska
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  2. Marcin Szmydt
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Corresponding author

Correspondence to Izabella Krzeminska .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. TIB Leibniz Information Center Science and Technology and University of Hannover, Hannover, Germany

    Sören Auer

  3. Poznań University of Economics and Business, Poznan, Poland

    Milena Stróżyna

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Krzeminska, I., Szmydt, M. (2022). Personality Based Data-Driven Personalization as an Integral Part of the Mobile Application. In: Abramowicz, W., Auer, S., Stróżyna, M. (eds) Business Information Systems Workshops. BIS 2021. Lecture Notes in Business Information Processing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04216-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-04216-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04215-7

  • Online ISBN: 978-3-031-04216-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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