Improving content popularity prediction with k-means clustering and deep-belief networks
Author:
Funder
University of Isfahan
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-020-10463-x.pdf
Reference36 articles.
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2. Alzubi, J., Nayyar, A., & Kumar, A. (2018). Machine learning from theory to algorithms: an overview. In Journal of physics: conference series, vol. 1142(1), IOP Publishing
3. Bao Z, Liu Y, Liu H, Zhang Z, et al (2017) Leveraging adaptive peeking window to improve Self-Exciting Point Process model for popularity prediction, IEEE Behavioral, Economic, Socio-cultural Computing (BESC), https://doi.org/10.1109/BESC.2017.8256373
4. Borghol Y, Mitra S, Ardon S et al (2011) Characterizing and modelling popularity of user-generated videos. Science Direct Performance Evaluation 68(11):1037–1055. https://doi.org/10.1016/j.peva.2011.07.008
5. Cha M, Kwak H, Rodriguez P (2009) Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems. IEEE/ACM Trans. on Networking 17(5):1357–1370. https://doi.org/10.1109/TNET.2008.2011358
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