# A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction

arXiv: https://arxiv.org/abs/1409.8276 · HTML: https://mlsanity.com/paper/1409.8276/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2014.

## Built on by (most cited first, 2 of 2)

- [An Adaptive Tensor-Train Decomposition Approach for Efficient Deep Neural Network Compression](https://mlsanity.com/paper/2408.01534/) (2024)
- [When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning](https://mlsanity.com/paper/2512.21486/) (2025)

## Abstract (arXiv)

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