# How robust is MovieLens? A dataset analysis for recommender systems

arXiv: https://arxiv.org/abs/1909.12799 · HTML: https://mlsanity.com/paper/1909.12799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [The Oracle and The Prism: A Decoupled and Efficient Framework for Generative Recommendation Explanation](https://mlsanity.com/paper/2511.16543/) (2025)

## Abstract (arXiv)

Research publication requires public datasets. In recommender systems, some datasets are largely used to compare algorithms against a --supposedly-- common benchmark. Problem: for various reasons, these datasets are heavily preprocessed, making the comparison of results across papers difficult. This paper makes explicit the variety of preprocessing and evaluation protocols to test the robustness of a dataset (or lack of flexibility). While robustness is good to compare results across papers, for flexible datasets we propose a method to select a preprocessing protocol and share results more transparently.