%0 Journal Article %1 info3-article-2019-10 %A Wassermann, Sarah %A Wehner, Nikolas %A Casas, Pedro %D 2019 %J ACM SIGMETRICS Performance Evaluation Review %K myown copy %N 3 %P 155-158 %T Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones %V 46 %X Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.

@article{info3-article-2019-10, abstract = {Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.}, added-at = {2019-10-08T15:21:25.000+0200}, author = {Wassermann, Sarah and Wehner, Nikolas and Casas, Pedro}, biburl = {https://www.bibsonomy.org/bibtex/29a3cdceb86852a7cb38d17ab1b0e53f1/uniwue_info3}, interhash = {dd5620c1fcd66672b4bde5dd5d85a6f5}, intrahash = {9a3cdceb86852a7cb38d17ab1b0e53f1}, journal = {ACM SIGMETRICS Performance Evaluation Review}, keywords = {myown copy}, month = {1}, number = 3, pages = {155-158}, timestamp = {2022-03-14T00:14:08.000+0100}, title = {Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones}, volume = 46, year = 2019 }