CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

TL;DR

This paper introduces CalBehav, a personalized machine learning model that dynamically predicts individual smartphone user responses to scheduled events using time-series data, improving communication management over static models.

Contribution

The paper presents a novel, context-aware, personalized behavioral model for smartphone users that adapts to individual differences and contextual variations in scheduled events.

Findings

CalBehav outperforms existing static models in predicting user responses.

The model effectively captures behavioral variations across different contexts.

Real datasets demonstrate improved management of incoming communications.

Abstract

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.