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

Abstract

IntroductionNowadays, smartphones are considered as essential devices in our daily life. Due to the recent advanced features in smartphones and the popularity of context-awareness in mobile technologies, individual's behavioral activities with their phones, such as phone call activities, mobile applications usage, mobile notification responses, social networking, and corresponding contextual information are recorded through the device logs. An individual smartphone's ability to store user's such diverse activities and associated contexts with their phones enables the study on data-driven smartphone usage behavior modeling and prediction [1]. In this paper, we aim to mine a set of personalized recent behavioral patterns, i.e., recency, based rules utilizing individual's contextual phone log data, for the purpose of building an effective context-aware personalized usage behavior prediction model. To illustrate the efficacy of our proposed approach, in this paper, we 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.

Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

“…Thus, to get huge amount of training data of individuals' phone usage, collecting contextual data year after year might not be meaningful to reflect one's present behavior. The reason is that individual's behavior changes over time and a period of recent data are more likely to be interesting and significant than older ones for predicting individual's future behavior in a particular context [79]. Consequently, individual's phone usage log data does not have too many samples and contextual features, requiring re-engineering of feature extraction in the deep neural network learning model.…”

ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

“…Although, association learning is another popular approach in the area of machine learning and data science and can be used for user behavioural analytics [7][8][9][10][11], we particularly focus on classification approach for the purpose of building a prediction model in this work. Classification learning techniques typically build the model using a given training dataset and then the resultant model can be used to predict the class label for a test case.…”

Text and phone calls: user behaviour and dual-channel communication prediction

“…However, these studies did not mention how long historical logs are required for better prediction. In a recent study, Sarkar et al [34] resolved the issue of choosing appropriate historical communication logs. They proved that only the most recent communication history (between 2 and 3 weeks) is sufficient to accurately predict the smartphone user's communication behaviour.…”