Seamless Transition: Mapping Smartphone-Derived Personality Profiles to Business Intelligence
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Abstract
This study explores the feasibility of transferring a personality prediction model, trained on
smartphone data, to a Business Intelligence (BI) system utilizing call logs. By leveraging the
Big Five personality traits model and machine learning techniques, the research addresses the
challenge of harmonizingdisparate data sources—richdataaboutsmartphoneapplicationsusage
and system settings, and aggregated call log data from a BI warehouse. Through meticulous data
preprocessing and feature engineering, the study evaluates the performance of various machine
learning algorithms, demonstrating that traits like Extraversion and Conscientiousness can be
reliably predicted. The study also introduces innovative methods for verifying predictions on
unlabeled BI data, including consistency over time and similarity of distributions. The findings
underscore the potential of integrating personality insights into BI systems, also highlighting the
ethical considerations and data privacy challenges inherent in such applications. This research
contribution lays the groundwork for future advancements in personality prediction models and
their practical business applications.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
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