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Hybrid machine learning approach for popularity prediction of newly released contents of online video streaming services

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  • Jeon, Hongjun
  • Seo, Wonchul
  • Park, Eunjeong
  • Choi, Sungchul

Abstract

In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical, not only for marketing but also for network usage. By successfully predicting user preferences, contents can be optimally deployed among servers which ultimately leads to network cost reduction. Many previous studies have predicted view-counts for this purpose. However, they normally make predictions based on historical view-count data from users, given the assumption that contents are already published to users. This can be a downside for newly released contents, which inherently does not have historical data. To address the problem, this research proposes a hybrid machine learning approach for the popularity prediction of unpublished video contents.

Suggested Citation

  • Jeon, Hongjun & Seo, Wonchul & Park, Eunjeong & Choi, Sungchul, 2020. "Hybrid machine learning approach for popularity prediction of newly released contents of online video streaming services," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:tefoso:v:161:y:2020:i:c:s004016252031129x
    DOI: 10.1016/j.techfore.2020.120303
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    References listed on IDEAS

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    1. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
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    Cited by:

    1. Jun, Seung-Pyo & Yoo, Hyoung Sun & Hwang, Jeena, 2021. "A hybrid recommendation model for successful R&D collaboration: Mixing machine learning and discriminant analysis," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    2. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).

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