  While the SLIM approach obtained high ranking-accuracy in many experiments in
the literature, it is also known for its high computational cost of learning
its parameters from data. For this reason, we focus in this paper on variants
of high-dimensional regression problems that have closed-form solutions.
Moreover, we motivate a re-scaling rather than a re-weighting approach for
dealing with biases regarding item-popularities in the data. We also discuss
properties of the sparse solution, and outline a computationally efficient
approximation. In experiments on three publicly available data sets, we
observed not only extremely reduced training times, but also significantly
improved ranking accuracy compared to SLIM. Surprisingly, various
state-of-the-art models, including deep non-linear autoencoders, were also
outperformed on two of the three data sets in our experiments, in particular
for recommendations with highly personalized relevance.
