  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.
