## Abstract

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 zero-truncated 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) side-information 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 "cold-start" 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, non-negative factor matrices in our model provide easy interpretability, and the tensor rank can be inferred from the data. We evaluate our model on several large-scale realworld binary tensors, achieving excellent computational scalability, and also demonstrate its usefulness in leveraging side-information provided in form of mode-network(s).

Original language | English (US) |
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Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015 |

Publisher | AUAI Press |

Pages | 375-384 |

Number of pages | 10 |

State | Published - Jan 1 2015 |

Externally published | Yes |