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.

本文研究了协同过滤问题中推荐给用户的物品之间的依赖关系模型，提出了一种基于高斯马尔科夫随机场的自动归一参数化和伪似然函数的方法，可以实现计算效率和推荐准确性之间的平衡。通过几个数据集的实验证明了该方法的有效性。