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The effect of quality metrics on the user watching behaviour in media content broadcast

Understanding the effects of quality metrics on the user behavior is important for the increasing number of content providers in order to maintain a competitive edge. The two data sets used are gathered from a provider of live streaming and a provider of video on demand streaming. The important quality and non quality features are determined by using both correlation metrics and relative importance determined by machine learning methods. A model that can predict and simulate the user behavior is developed and tested. A time series model, machine learning model and a combination of both are compared. Results indicate that both quality features and non quality features are important in understanding user behavior, and the importance of quality features are reduced over time. For short prediction times the model using quality features is performing slightly better than the model not using quality features.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-304514
Date January 2016
CreatorsSetterquist, Erik
PublisherUppsala universitet, Avdelningen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationUPTEC F, 1401-5757 ; 16048

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