Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes

TL;DR

This paper introduces a reinforced Poisson process model to explicitly capture and predict the popularity dynamics of individual items, demonstrating superior accuracy over existing methods through extensive experiments.

Contribution

The paper presents a novel probabilistic framework using reinforced Poisson processes for modeling and predicting item popularity, with Bayesian enhancements for improved accuracy.

Findings

Outperforms existing popularity prediction methods

Effectively models the arrival process of popularity

Demonstrates robustness on citation datasets

Abstract

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.