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SMAUG: Semantic-Enhanced Mobile App Usage Data Generation With LLM
5 references·0 citing·12 second hop
SMAUG: Semantic-Enhanced Mobile App Usage Data Generation With LLM (2025)SMAUG: Semantic-Enhanced Mobile App Usage…You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior (2023)You Are How You Use Apps: Use…Finding Spatiotemporal Patterns of Mobile Application Usage (2021)Finding Spatiotemporal Patter…The Effect of the GDPR on Privacy Policies (2020)The Effect of the GDPR on Pri…User profiling from their use of smartphone applications: A survey (2019)User profiling from their use…Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations (2018)Mining mobile application usa…Discovering human routines from cell phone data with topic models (2008)Where and what: Using smartphones to predict next locations and applications in daily life (2013)The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. (1999)Subjective well-being: The science of happiness and a proposal for a national index. (2000)Facebook Profiles Reflect Actual Personality, Not Self-Idealization (2010)Gender Profiling From a Single Snapshot of Apps Installed on a Smartphone: An Empirical Study (2019)A Survey of Technologies and Recent Developments for Sustainable Smart Cycling (2021)CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service (2019)Rethinking privacy for avatars: biometric and inferred data in the metaverse (2025)Discovering regions of different functions in a city using human mobility and POIs (2012)Predicting Personality Using Novel Mobile Phone-Based Metrics (2013)Predicting personality from patterns of behavior collected with smartphones (2020)
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References

  • 2020The Effect of the GDPR on Privacy Policies176 cited
  • 2019User profiling from their use of smartphone applications: A survey60 cited
  • 2023You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior54 cited
  • 2021Finding Spatiotemporal Patterns of Mobile Application Usage33 cited
  • 2018Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations10 cited

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