A Video Popularity Prediction Scheme with Attention-Based LSTM and Feature Embedding

Predicting the popularity of online contents especially videos has drawn a lot of attention recently, since successful prediction of popularity can benefit many practical applications such as recommender systems and proactive caching, and help optimize the advertisement strategies or balance the throughput in the network. In this paper, we formulate a popularity prediction problem and present an Attention-based Long Short Term Memory (LSTM) with Feature Embedding method (ALFE) to tackle the popularity prediction problem. Several features including publish time, follower count, and type of the video are considered and compared. Experiments on a real world dataset show that our method outperforms other competitive baselines from existing works in terms of prediction accuracy. The attention mechanism and feature embedding contribute to the improvement of accuracy. Among all the features, timestamp of popularity and video duration are shown to be the most informative ones, due to the regularity and periodicity of human daily activities.