Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Adaptive Modeling of a User's Daily Life with a Wearable Sensor Network
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Activity-aware map: identifying human daily activity pattern using mobile phone data
HBU'10 Proceedings of the First international conference on Human behavior understanding
Bursts: The Hidden Pattern Behind Everything We Do
Bursts: The Hidden Pattern Behind Everything We Do
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
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This paper investigated the relationship between incrementally logged phone logs and self-reported survey data to derive regularity and predictability from mobile phone usage logs. First, we extracted information not from a single value such as location or call logs, but from multivariate contextual logs. Then we considered the changing pattern of the incrementally logged information over time. To evaluate the patterns of human behavior, we applied entropy changes and the duplicated instances ratios from the stream of mobile phone usage logs. By applying the Hidden Markov Models to the patterns, the accumulated log patterns were classified according to the self-reported survey data. This research confirmed that regularity and predictability of human behavior can be evaluated by mobile phone usages.