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Probabilistic approaches for tensor factorization aim to extract meaningfulstructure from incomplete data by postulating low rank constraints. Recently,variational Bayesian (VB) inference techniques have successfully been appliedto large scale models. This paper presents full Bayesian inference via VB onboth single and coupled tensor factorization models. Our method can be run evenfor very large models and is easily implemented. It exhibits better predictionperformance than existing approaches based on maximum likelihood on severalreal-world datasets for missing link prediction problem.

A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction