logo

SCIENCE CHINA Information Sciences, Volume 61, Issue 1: 018101(2018) https://doi.org/10.1007/s11432-016-9078-0

Accurate inference of user popularity preference in a large-scale online video streaming system

More info
  • ReceivedMar 21, 2016
  • AcceptedMar 16, 2017
  • PublishedJul 12, 2017

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61271199, 61301082, 61572071).


References

[1] Goel S, Broder A, Gabrilovich E, et al. Anatomy of the long tail: ordinary people with extraordinary tastes. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York, 2010. 201--210. Google Scholar

[2] Oh J, Park S, Yu H, et al. Novel recommendation based on personal popularity tendency. In: Proceedings of the 11th International Conference on Data Mining (ICDM), Vancouver, 2011. 507--516. Google Scholar

[3] Su X, Khoshgoftaar T M. A survey of collaborative filtering techniques. Adv Artif Intell, 2009, 2009: 1-19 CrossRef Google Scholar

[4] Zhang S, Wang W, Ford J, et al. Using singular value decomposition approximation for collaborative filtering. In: Proceeding of the IEEE International Conference on E-Commerce Technology, Munchen, 2005. 257--264. Google Scholar

[5] Zhang S, Wang W, Ford J, et al. Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of SIAM International Conference on Data Mining, Bethesda, 2006. 549--553. Google Scholar

  • Figure 1

    (Color online) Distributions of the three characteristics, (a) median, (b) CV and (c) relative skewness, of the PP sequences in PPTV and that in the null model. (d) is the PP inference accuracy of our proposed algorithms and the baseline algorithms.

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1