期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 20, 期 6, 页码 1512-1524出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2017.2769807
关键词
User view preference; view count; movie rating; comments
资金
- National Key R&D Program of China [2016YFB0201900]
- National Natural Science Foundation of China [61572538]
- Fundamental Research Funds for the Central Universities [17LGJC23]
- Australian Research Council [DP150103658]
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.
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