Markov Random Fields for Collaborative Filtering
This work addresses recommendation accuracy and efficiency in collaborative filtering, presenting an incremental improvement by connecting MRFs to existing methods like autoencoders.
The paper tackled the collaborative filtering problem by modeling item dependencies with a Gaussian Markov Random Field, achieving competitive ranking accuracy across three datasets and a 20% gain on the largest dataset while using only a small fraction of training time compared to baselines.
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon Besag's auto-normal parameterization and pseudo-likelihood, which not only enables computationally efficient learning, but also connects the areas of MRFs and sparse inverse covariance estimation with autoencoders and neighborhood models, two successful approaches in collaborative filtering. We propose a novel approximation for learning sparse MRFs, where the trade-off between recommendation-accuracy and training-time can be controlled. At only a small fraction of the training-time compared to various baselines, including deep nonlinear models, the proposed approach achieved competitive ranking-accuracy on all three well-known data-sets used in our experiments, and notably a 20% gain in accuracy on the data-set with the largest number of items.