# Collaborative Filtering via High-Dimensional Regression

arXiv: https://arxiv.org/abs/1904.13033 · HTML: https://mlsanity.com/paper/1904.13033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems](https://mlsanity.com/paper/2106.10679/) (2021)
- [What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems](https://mlsanity.com/paper/2508.21547/) (2025)
- [Does Weighting Improve Matrix Factorization for Recommender Systems?](https://mlsanity.com/paper/2510.10440/) (2025)

## Abstract (arXiv)

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.