An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to model explicitly the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.
Shen et al. proposed a reinforced Poisson process to model explicitly the dynamic popularity of individual items based on the arrival time of attention REF .
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
ArXiv
ArXiv
Computer Science Physics