Results 1 
1 of
1
ZeroTruncated Poisson Tensor Factorization for Massive Binary Tensors
"... We present a scalable Bayesian model for lowrank factorization of massive tensors with binary observations. The proposed model has the following key properties: (1) in contrast to the models based on the logistic or probit likelihood, using a zerotruncated Poisson likelihood for binary data al ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
We present a scalable Bayesian model for lowrank factorization of massive tensors with binary observations. The proposed model has the following key properties: (1) in contrast to the models based on the logistic or probit likelihood, using a zerotruncated Poisson likelihood for binary data allows our model to scale up in the number of ones in the tensor, which is especially appealing for massive but sparse binary tensors; (2) sideinformation in form of binary pairwise relationships (e.g., an adjacency network) between objects in any tensor mode can also be leveraged, which can be especially useful in “coldstart ” settings; and (3) the model admits simple Bayesian inference via batch, as well as online MCMC; the latter allows scaling up even for dense binary data (i.e., when the number of ones in the tensor/network is also massive). In addition, nonnegative factor matrices in our model provide easy interpretability, and the tensor rank can be inferred from the data. We evaluate our model on several largescale realworld binary tensors, achieving excellent computational scalability, and also demonstrate its usefulness in leveraging sideinformation provided in form of modenetwork(s).