Neural Information Processing Systems http://nips.cc/

Based on the feedback, we will try to re-write this section more clearly as well.

Collaborative filtering has witnessed significant improvem ents in recent years, largely due to models based on low-dimensional embeddings, like weighted matrix factorizati on (e.g., [26, 39]) and deep learning [23, 22, 33, 47, 62, 58, 20, 11], including autoencoders [58, 33]. Also neighborhoo d-based approaches are competitive in certain regimes (e.g., [1, 53, 54]), despite being simple heuristics based o n item-item (or user-user) similarity matrices (like cosin e similarity). In this paper, we outline that Markov Random Fi elds (MRF) are closely related to autoencoders as well as to neighborhood-based approaches. W e build on the enormo us progress made in learning MRFs, in particular in sparse inverse covariance estimation (e.g., [36, 59, 15, 2, 60, 44, 45, 63, 55, 24, 25, 52, 56, 51]). Much of the literature on sparse inverse covariance estimation focuses on the regi me where the number of data points n is much smaller than the number of variables m in the model ( n m).

Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show that its training objective has a closed-form solution, and discuss the resulting conceptual insights. Surprisingly, this simple model achieves better ranking accuracy than various state-of-the-art collaborative-filtering approaches, including deep non-linear models, on most of the publicly available data-sets used in our experiments.