TY - JOUR
T1 - Exploring Popularity Predictability of Online Videos with Fourier Transform
AU - Zhou, Yan
AU - Wu, Zhanpeng
AU - Zhou, Yipeng
AU - Hu, Miao
AU - Yang, Chunfeng
AU - Qin, Jing
N1 - Funding Information:
This work was supported in part by the National Key R&D Program of China under Grant 2018YFB0204100, in part by the Nature Science Foundation of China under Grant 61572538 and Grant 61802452, in part by the Guangdong Special Support Program under Grant 2017TX04X148, in part by the Natural Science Foundation of Guangdong under Grant 2018A030310079, in part by the China Postdoctoral Science Foundation under Grant 2018M631025, in part by the Australian Research Council under Grant DE180100950, and in part by the Hong Kong Innovation and Technology Commission under Grant ITS/319/17.
Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - The prediction of video popularity is of significant importance to online video service providers in terms of resource provisioning, online advertisement, and video recommendation. Traditional approaches normally utilize videos' historical popularity traces to make such a prediction. However, it is still uncertain whether the future popularity of a video is sure to be associated with its past popularity. In this paper, we explore the problem of video popularity predictability by analyzing videos' view count traces in the frequency domain with Fourier transform. We observe that sharp turns (e.g., peaks and valleys) of view count traces cause the inaccuracy in popularity prediction, which can be seized and quantified by high-frequency components in the frequency domain. Based on the ratio of high-frequency energy, videos can be classified as the fluctuating group, which is hard for prediction, and the smooth group, which is friendly for prediction. The result is further verified via experiments with state-of-the-art predictive algorithms. Inspired by our findings, we propose a strategy to improve prediction performance by removing out-of-date traces before each sharp turn because it is highly possible that the popularity evolution trend has been altered at each sharp turn. To the end, we compare the prediction issue between videos and microblogs. Surprisingly, most microblog traces are smooth. We conjecture that video providers' recommendation and promotion strategies are prone to causing sharp turns in view count traces. In contrast, there is no such initiative counterpart on microblog platforms changing trace evolution of microblogs frequently.
AB - The prediction of video popularity is of significant importance to online video service providers in terms of resource provisioning, online advertisement, and video recommendation. Traditional approaches normally utilize videos' historical popularity traces to make such a prediction. However, it is still uncertain whether the future popularity of a video is sure to be associated with its past popularity. In this paper, we explore the problem of video popularity predictability by analyzing videos' view count traces in the frequency domain with Fourier transform. We observe that sharp turns (e.g., peaks and valleys) of view count traces cause the inaccuracy in popularity prediction, which can be seized and quantified by high-frequency components in the frequency domain. Based on the ratio of high-frequency energy, videos can be classified as the fluctuating group, which is hard for prediction, and the smooth group, which is friendly for prediction. The result is further verified via experiments with state-of-the-art predictive algorithms. Inspired by our findings, we propose a strategy to improve prediction performance by removing out-of-date traces before each sharp turn because it is highly possible that the popularity evolution trend has been altered at each sharp turn. To the end, we compare the prediction issue between videos and microblogs. Surprisingly, most microblog traces are smooth. We conjecture that video providers' recommendation and promotion strategies are prone to causing sharp turns in view count traces. In contrast, there is no such initiative counterpart on microblog platforms changing trace evolution of microblogs frequently.
KW - Fourier transform
KW - Popularity predictions
KW - video classification
UR - http://www.scopus.com/inward/record.url?scp=85064836589&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2907929
DO - 10.1109/ACCESS.2019.2907929
M3 - Journal article
AN - SCOPUS:85064836589
SN - 2169-3536
VL - 7
SP - 41823
EP - 41834
JO - IEEE Access
JF - IEEE Access
M1 - 6287639
ER -