Deep Poisson Factorization Machines: factor analysis for mapping behaviors in journalist ecosystem

TL;DR

This paper introduces Deep Poisson Factorization Machines, a novel deep Bayesian model for analyzing and characterizing journalist behaviors in online ecosystems by modeling temporal and label information.

Contribution

It develops a deep Bayesian Poisson factorization model with scalable inference methods, extending matrix factorization to capture complex temporal and label-dependent interactions.

Findings

Model effectively captures latent journalist behaviors.

Scalable inference methods enable handling large datasets.

Potential for improved understanding of information feedback loops.

Abstract

Newsroom in online ecosystem is difficult to untangle. With prevalence of social media, interactions between journalists and individuals become visible, but lack of understanding to inner processing of information feedback loop in public sphere leave most journalists baffled. Can we provide an organized view to characterize journalist behaviors on individual level to know better of the ecosystem? To this end, I propose Poisson Factorization Machine (PFM), a Bayesian analogue to matrix factorization that assumes Poisson distribution for generative process. The model generalizes recent studies on Poisson Matrix Factorization to account temporal interaction which involves tensor-like structure, and label information. Two inference procedures are designed, one based on batch variational EM and another stochastic variational inference scheme that efficiently scales with data size. An important novelty in this note is that I show how to stack layers of PFM to introduce a deep architecture. This work discusses some potential results applying the model and explains how such latent factors may be useful for analyzing latent behaviors for data exploration.