RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

Abstract

Abstract Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users’ diverse behavioral activities with their phones and associated contexts are recorded through the device logs. Behavioral patterns of smartphone users may vary greatly between individuals in different contexts—for example, temporal, spatial, or social contexts. However, an individual’s phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user. Thus, an individual’s recent behavioral patterns and corresponding machine learning rules are more likely to be interesting and significant than older ones for modeling and predicting their phone usage behavior. Based on this concept of recency, in this paper, we present an approach for mining recency-based personalized behavior, and name it “RecencyMiner” for short, utilizing individual’s contextual smartphone data, in order to build a context-aware personalized behavior prediction model. The effectiveness of RecencyMiner is examined by considering individual smartphone user’s real-life contextual datasets. The experimental results show that our proposed recency-based approach better predicts individual’s phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.

Keywords

• Mobile data mining
• Machine learning
• Association rule learning
• Data science
• User behavior modeling
• Context-awareness