%0 Conference Paper %1 info3-inproceedings-2018-54 %A Wassermann, Sarah %A Wehner, Nikolas %A Casas, Pedro %B WAIN - Workshop on AI in Networks and Distributed Systems %C Toulouse, France %D 2018 %K myown old %T Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones %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.

@inproceedings{info3-inproceedings-2018-54, 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 = {2021-02-08T10:00:33.000+0100}, address = {Toulouse, France}, author = {Wassermann, Sarah and Wehner, Nikolas and Casas, Pedro}, biburl = {https://www.bibsonomy.org/bibtex/20daa186ee474c9cc14dc1abc98f535a4/uniwue_info3}, booktitle = {WAIN - Workshop on AI in Networks and Distributed Systems}, interhash = {4f42d4d13df4190018acc6c0fb1bf1a0}, intrahash = {0daa186ee474c9cc14dc1abc98f535a4}, keywords = {myown old}, month = {11}, timestamp = {2022-03-14T00:13:53.000+0100}, title = {Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones}, year = 2018 }