Collaborative Filtering via High-Dimensional Regression
This work addresses efficiency and accuracy challenges in collaborative filtering for recommendation systems, though it appears incremental as it builds on existing regression methods.
The paper tackles the high computational cost of SLIM in collaborative filtering by proposing high-dimensional regression variants with closed-form solutions and a re-scaling approach for item-popularity biases, resulting in extremely reduced training times and significantly improved ranking accuracy compared to SLIM and outperforming state-of-the-art models on two of three data sets.
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.