Towards a predictive cache replacement strategy for multimedia content
Journal of Network and Computer Applications
On popularity prediction of videos shared in online social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Predicting the life cycle and the short-term popularity of a Web object is important for network architecture optimization. In this paper, we attempt to predict the popularity of a Web object given its historical access records using a novel neural network technique, reservoir computing (RC). The traces of popular videos at YouTube for five continuous months are taken as a case study. We compare RC with existing analytical models. Experimental results show that RC, given a 10-day trace composed of daily cumulative views for a video, is able to predict the next-day’s popularity with less than 5% relative square errors (RSEs). It is also demonstrated that RC achieves the best prediction performance among all compared models in longer-term prediction. The advantages and limitations of using RC in popularity prediction are discussed.