Predictive discrete latent factor models for large scale dyadic data

Abstract

A method for predicting future responses from large sets of dyadic data includes measuring a dyadic response variable associated with a dyad from two different sets of data; measuring a vector of covariates that captures the characteristics of the dyad; determining one or more latent, unmeasured characteristics that are not determined by the vector of covariates and which induce local structures in a dyadic space defined by the two different sets of data; and modeling a predictive response of the measurements as a function of both the vector of covariates and the one or more latent characteristics, wherein modeling includes employing a combination of regression and matrix co-clustering techniques, and wherein the one or more latent characteristics provide a smoothing effect to the function that produces a more accurate and interpretable predictive model of the dyadic space that predicts future dyadic interaction based on the two different sets of data.