A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction
This work provides an incremental improvement in probabilistic tensor factorization for link prediction tasks.
The paper tackles the problem of missing link prediction by developing a full Bayesian inference method for single and coupled tensor factorization models using variational Bayesian techniques, achieving better prediction performance than maximum likelihood approaches on several real-world datasets.
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.