TY - GEN

T1 - Prediction of Next App in OS

AU - Ma, Ruimin

AU - Zhang, Yuyi

AU - Liu, Jing

AU - Petrosian, Ovanes

AU - Krinkin, Kirill

N1 - Publisher Copyright: © 2022 IEEE.

PY - 2022/6/16

Y1 - 2022/6/16

N2 - With the popularity of smart devices, application responsiveness is one of the most important indicators of user experience, and forecast the next application will be used by users is crucial to the management planning for related companies. In this paper, more sophisticated machine learning, and even deep learning, which can achieve better forecast performance, is widely used in this field. However, the opacity of these black-box models greatly limits user trust and how well developers can optimize the model. To address these issues, this paper first tests six of the most popular forecasting algorithms, including ensemble models and neural networks, to select the optimal model. As an innovation, this paper also uses XAI techniques to explain black-box models to increase user trust in the results generated by forecast models and to help developers in their work. After completing the above work, on the one hand, we found that the ensemble model performed better in the time series datasets with user application usage information, especially with LightGBM, on the other hand, we found that the prediction model using the SHAP method showed that the target variable categorical features and lags Features are important features to forecast the user's next application.

AB - With the popularity of smart devices, application responsiveness is one of the most important indicators of user experience, and forecast the next application will be used by users is crucial to the management planning for related companies. In this paper, more sophisticated machine learning, and even deep learning, which can achieve better forecast performance, is widely used in this field. However, the opacity of these black-box models greatly limits user trust and how well developers can optimize the model. To address these issues, this paper first tests six of the most popular forecasting algorithms, including ensemble models and neural networks, to select the optimal model. As an innovation, this paper also uses XAI techniques to explain black-box models to increase user trust in the results generated by forecast models and to help developers in their work. After completing the above work, on the one hand, we found that the ensemble model performed better in the time series datasets with user application usage information, especially with LightGBM, on the other hand, we found that the prediction model using the SHAP method showed that the target variable categorical features and lags Features are important features to forecast the user's next application.

KW - Application

KW - Ensemble model

KW - Explainable AI

KW - Neural network

KW - Time-series forecasting

UR - http://www.scopus.com/inward/record.url?scp=85134341981&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/d0485781-f7a5-3e09-b4d2-68946279a4cb/

U2 - 10.1109/neuront55429.2022.9805534

DO - 10.1109/neuront55429.2022.9805534

M3 - Conference contribution

AN - SCOPUS:85134341981

SN - 9781665467766

T3 - Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

SP - 28

EP - 31

BT - Proceedings of 2022 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

A2 - Shaposhnikov, S.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

Y2 - 16 June 2022

ER -