jumabek/receptivity

Receptivity

Project Overview

This project explores the power of feature engineering in the context of opportune moment detection for interrupting smartphone users. It introduces a novel feature engineering approach named Contextual Filtered Features (CFF) and includes a robust machine learning setting for proper evaluation, such as using Leave-One-Subject-Out.

Installation and Setup

1. Clone the repository and navigate to the project folder:

git clone [email protected]:Jumabek/receptivity.git cd receptivity

2. Link your local Kemophone data folder: ln -s /home/juma/dataonssd/kemophone data/

Usage

Explore the Jupyter notebooks located in the analysis folder for insights into the data analysis and feature engineering process.

Data Description

[Description of the data structure, content, and any important details.]

Citing Our Work

If you use our data or code for your research, please cite our paper:

@article{alikhanov2023design,
title={Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction},
author={Alikhanov, Jumabek and Zhang, Panyu and Noh, Youngtae and Kim, Hakil},
journal={IEEE Internet of Things Journal},
year={2023},
publisher={IEEE}
}

Link to paper

License

This project is open-sourced under a free license.

Contact

For questions or collaboration opportunities, contact [email protected].

Acknowledgments

This research outcome would not be possible without the support and mentoring of Prof. Uichin Lee from KAIST.