Seamless Transition: Mapping Smartphone-Derived Personality Profiles to Business Intelligence - Rzeźnik, Jakub; Krzemińska, Izabella (Technium: Romanian Journal of Applied Sciences and Technology, 2024)
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.