# A Scale-Consistent Approach for Recommender Systems

arXiv: https://arxiv.org/abs/1905.00055 · HTML: https://mlsanity.com/paper/1905.00055/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems](https://mlsanity.com/paper/2204.01815/) (2022)
- [A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System](https://mlsanity.com/paper/2206.13355/) (2022)

## Abstract (arXiv)

In this paper we propose and develop a relatively simple and efficient approach for estimating unknown elements of a user-rating matrix in the context of a recommender system (RS). The critical theoretical property of the method is its consistency with respect to arbitrary units implicitly adopted by different users to construct their quantitative ratings of products. It is argued that this property is needed for robust performance accuracy across a broad spectrum of RS application domains.