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

Hongjun Jeon, Wonchul Seo, Eunjeong Park and Sungchul Choi

Technological Forecasting and Social Change, 2020, vol. 161, issue C

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 To address the problem, this research proposes a hybrid machine learning approach for the popularity prediction of unpublished video contents.

Keywords: Streaming service; Popularity prediction; Embeddings; Deep learning; Gradient boosting decision tree (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:161:y:2020:i:c:s004016252031129x

DOI: 10.1016/j.techfore.2020.120303

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