Modeling and predicting popularity dynamics via reinforced Poisson Processes

Huawei Shen, Dashun Wang, Chaoming Song, Albert László Barabási

Research output: Chapter in Book/Report/Conference proceedingConference contribution

84 Scopus citations

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 explicitly model 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
PublisherAI Access Foundation
Pages291-297
Number of pages7
ISBN (Electronic)9781577356776
StatePublished - Jan 1 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Shen, H., Wang, D., Song, C., & Barabási, A. L. (2014). Modeling and predicting popularity dynamics via reinforced Poisson Processes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (pp. 291-297). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.