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.

本文采用高维回归问题的解析解取代计算开销大的 SLIM 方法，通过重新缩放处理 item-popularities 偏差，得到了稀疏解的性质，并且在三个公共数据集的实验中，相对于 SLIM 在准确度和训练时间上均有显著提高，同时在高度个性化推荐方面，还优于多种最新的非线性自动编码器等模型。