Larm

Online content popularity prediction provides substantial value to a broad range of applications in the end-to-end social media systems, from network resource allocation to targeted advertising. While using historical popularity can predict the near-term popularity with a reasonable accuracy, the bursty nature of online content popularity evolution makes it difficult to capture the correlation between historical data and future data in the long term. Although various existing efforts have been made toward long-term prediction, they need to accumulate a long enough historical data before the prediction and their model assumptions cannot be applied to the complex YouTube networks with inherent unpredictability.