Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones

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 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.
Fichier principal
Vignette du fichier
WAIN_2018_camera_ready_paper_17.pdf (337.64 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01898083 , version 1 (18-10-2018)

Identifiers

  • HAL Id : hal-01898083 , version 1

Cite

Sarah Wassermann, Nikolas Wehner, Pedro Casas. Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones. Workshop on AI in Networks (WAIN) 2018, Dec 2018, Toulouse, France. ⟨hal-01898083⟩

Collections

INRIA INRIA2
143 View
1087 Download

Share

Gmail Mastodon Facebook X LinkedIn More