Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones
In: Workshop on AI in Networks (WAIN) 2018 ; https://inria.hal.science/hal-01898083 ; Workshop on AI in Networks (WAIN) 2018, Dec 2018, Toulouse, France, 2018
Online
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Zugriff:
International audience ; 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 user-behavior-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.
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Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones
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Autor/in / Beteiligte Person: | Wassermann, Sarah ; Wehner, Nikolas ; Casas, Pedro ; Middleware on the Move (MIMOVE) ; Inria de Paris ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) ; Austrian Institute of Technology Vienna (AIT) |
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Zeitschrift: | Workshop on AI in Networks (WAIN) 2018 ; https://inria.hal.science/hal-01898083 ; Workshop on AI in Networks (WAIN) 2018, Dec 2018, Toulouse, France, 2018 |
Veröffentlichung: | HAL CCSD, 2018 |
Medientyp: | Konferenz |
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