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Exploring Popularity Predictability of Online Videos with Fourier Transform


3D Reconstruction in Canonical

Audience-Retention-Rate-Aware
Abstract


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 videosā?? historical popularity traces to make such prediction. However, it is still uncertain whether future popularity of a video is surely to be associated with its past popularity. In this paper, we explore the problem of video popularity predictability by analyzing videosā?? view count traces in 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 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 Inspired by our findings, we propose a strategy to improve prediction performance by removing out-of-date traces before each sharp turn because it is highly possible that the popularity evolution trend has been altered at each sharp turn.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 providersā?? 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

KeyWords
popularity predictions, Fourier transform, video classification



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