Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes

By Huawei Shen, Dashun Wang, Chaoming Song, Albert-László Barabási. AAAI Conference on Artificial Intelligence, 2014

321 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/11bd4ee4993ae89dafaed935f5749df6b50f780a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qi Cao
2. Charu C. Aggarwal
3. Huawei Shen
4. Qingyuan Zhao
5. Xueqi Cheng
6. Cheng Li
7. Jure Leskovec
8. Qiaozhu Mei

Citations in the corpus

• Cross-Domain Recommendation: An Embedding and Mapping Approach (2017, background): “With the explosively growing amount of online information, recommender system becomes an important tool to help users efficiently find their desired information [Adomavicius and Tuzhilin, 2005; Shen et al., 2014].”
• DeepCas: An End-to-end Predictor of Information Cascades (2016, background): “Cascades of particular types of information are empirically proved to be predictable to some extent, including Tweets/microblogs [43, 39, 20, 11, 17, 44], photos [10], videos [2] and academic papers [31].”
• SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “Our S EISMIC method is also significantly faster than the RPM model [28], which requires to solve a nonlinear optimization problem every time it predicts.”
• DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades (2017, methodology): “Shen et al. [9] employed Reinforced Poisson Processes (RPP) to model the three phenomena in social networks: attractiveness of an item, a temporal relaxation of attractiveness, and the rich-get-richer mechanism.”