Predicting quality of experience of popular mobile applications from a living lab study
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Predicting quality of experience of popular mobile applications from a living lab study. / Masi, Alexandre De; Wac, Katarzyna.
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Predicting quality of experience of popular mobile applications from a living lab study
AU - Masi, Alexandre De
AU - Wac, Katarzyna
PY - 2019
Y1 - 2019
N2 - In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications' QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94 0.77 accuracy. Surprisingly, after the following top three features:± session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.
AB - In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications' QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94 0.77 accuracy. Surprisingly, after the following top three features:± session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.
KW - Context
KW - Mobile Applications
KW - QoE Prediction
KW - Quality of Experience
KW - Quality of Service
UR - http://www.scopus.com/inward/record.url?scp=85068703725&partnerID=8YFLogxK
U2 - 10.1109/QoMEX.2019.8743306
DO - 10.1109/QoMEX.2019.8743306
M3 - Article in proceedings
BT - 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
PB - IEEE
T2 - 11th International Conference on Quality of Multimedia Experience, QoMEX 2019
Y2 - 5 June 2019 through 7 June 2019
ER -
ID: 235477183