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Predicting session length in media streaming
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-8180-7521
Pandora Media Inc, USA.
Pandora Media Inc, USA.
Pandora Media Inc, USA.
2017 (English)In: SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, p. 977-980Conference paper, Published paper (Refereed)
Abstract [en]

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads. In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service.We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline. © 2017 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
2017. p. 977-980
Keywords [en]
Dwell Time, Session Length, Survival Analysis, User Behavior, Behavioral research, Bioinformatics, Forecasting, Information retrieval, Trees (mathematics), Length distributions, Media streaming services, User behaviors, User interaction, User's satisfaction, Media streaming
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33211DOI: 10.1145/3077136.3080695Scopus ID: 2-s2.0-85029395373ISBN: 9781450350228 (print)OAI: oai:DiVA.org:ri-33211DiVA, id: diva2:1179212
Conference
40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, 7 August 2017 through 11 August 2017
Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2019-01-22Bibliographically approved

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Vasiloudis, Theodore

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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