We consider the recovery of a low rank real-valued matrix M given a subset of noisy discrete
(or quantized) measurements. Such problems arise in several applications such as …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses
interpretable components to model the forces that drive how a customer chooses products; …

This paper proposes CF-NADE, a neural autoregressive architecture for collaborative
filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF …

We develop hierarchical Poisson matrix factorization (HPF), a novel method for providing
users with high quality recommendations based on implicit feedback, such as views, clicks …

This work provides the first unifying theoretical framework for node (positional) embeddings
and structural graph representations, bridging methods like matrix factorization and graph …

Abstract We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country–
country interaction event data. These data consist of interaction events of the form “country i …

The dimension of the parameter space is typically unknown in a variety of models that rely
on factorizations. For example, in factor analysis the number of latent factors is not known …

Poisson factorization is a probabilistic model of users and items for recommendation
systems, where the so-called implicit consumer data is modeled by a factorized Poisson …

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic
count matrix, whose columns are sequentially observed count vectors. The model builds a …