Statistical study of view preferences for online videos with cross-platform information

Yipeng Zhou, Xuhong Gu, Di Wu, Min Chen, Terence H. Chan, Siu wai Ho

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


The knowledge of view preferences of users is crucial for online video providers to improve their system operations and video recommendations. However, it is challenging to accurately acquire this knowledge by merely relying on a single online video system. In this paper, we conduct a joint statistical study using the cross-platform information obtained from Douban, the largest online video database with video rating functionality in China, and Youku, one of the largest online video streaming systems in China. The Douban dataset includes feedbacks (e.g., movie ratings, comments, and reviews) from all users of different online video systems, and movie metadata (e.g., release date, actors, and directors), based on which we can statistically explore effective and significant factors attributing to video view counts. Meanwhile, our study unveils user behaviors that are latent when only observing a single video system. Finally, a multiple correlation analysis reveals that factors extracted from Douban can significantly increase our ability to predict video view counts. Our study can benefit video caching, video procurement, and advertisement campaign for online video providers.

Original languageEnglish
Pages (from-to)1512-1524
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number6
Early online date3 Nov 2017
Publication statusPublished - Jun 2018
Externally publishedYes


  • user view preference
  • video count
  • movie rating
  • comments
  • User view preference
  • view count


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