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 -