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We present a scalable Bayesian model for low-rank factorization of massivetensors with binary observations. The proposed model has the following keyproperties: (1) in contrast to the models based on the logistic or probitlikelihood, using a zero-truncated Poisson likelihood for binary data allowsour model to scale up in the number of \emph{ones} in the tensor, which isespecially appealing for massive but sparse binary tensors; (2)side-information in form of binary pairwise relationships (e.g., an adjacencynetwork) between objects in any tensor mode can also be leveraged, which can beespecially useful in "cold-start" settings; and (3) the model admits simpleBayesian inference via batch, as well as \emph{online} MCMC; the latter allowsscaling up even for \emph{dense} binary data (i.e., when the number of ones inthe tensor/network is also massive). In addition, non-negative factor matricesin our model provide easy interpretability, and the tensor rank can be inferredfrom the data. We evaluate our model on several large-scale real-world binarytensors, achieving excellent computational scalability, and also demonstrateits usefulness in leveraging side-information provided in form ofmode-network(s).

Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors