Markov Random Fields for Collaborative Filtering

Harald Steck

Abstract: 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 parametrization 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 tradeoff between recommendation-accuracy and training-time can be controlled. At only a small fraction of the training-time compared to various baselines, including deep non-linear models, the proposed approach achieved competitive ranking-accuracy on all the 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.

Code Link: https://github.com/hasteck/MRF_NeurIPS_2019

CMT Num: 2928