Identifying Recent Behavioral Data Length in Mobile Phone Log
Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han

TL;DR
This paper proposes a method to dynamically identify the most recent segment of mobile phone logs, emphasizing recent behavioral patterns for improved behavior prediction, marking a novel approach in recency-based modeling.
Contribution
It introduces the first dynamic approach to determine recent behavioral data length from phone logs for behavior modeling.
Findings
First dynamic recent log identification method.
Improves recency-based behavior prediction.
Addresses the variability in individual phone usage patterns.
Abstract
Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individ- ual's diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and significant than older ones for predicting in- dividual's behavior. The goal of this poster paper is to iden- tify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the first dynamic recent log-based study that takes into account individual's recent behavioral patterns for modeling their phone call behaviors.
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Opportunistic and Delay-Tolerant Networks · Complex Network Analysis Techniques
