# e-Fold Cross-Validation for Recommender-System Evaluation

arXiv: https://arxiv.org/abs/2412.01011 · HTML: https://mlsanity.com/paper/2412.01011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [LensKit for Python: Next-Generation Software for Recommender System Experiments](https://mlsanity.com/paper/1809.03125/) (2018)
- [From Clicks to Carbon: The Environmental Toll of Recommender Systems](https://mlsanity.com/paper/2408.08203/) (2024)
- [Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance](https://mlsanity.com/paper/2410.09359/) (2024)
- [Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation](https://mlsanity.com/paper/2307.13910/) (2023)
- [EMERS: Energy Meter for Recommender Systems](https://mlsanity.com/paper/2409.15060/) (2024)
- [Adversarial Collaborative Filtering for Free](https://mlsanity.com/paper/2308.13541/) (2023)

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

- [Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?](https://mlsanity.com/paper/2502.14297/) (2025)
- [Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems](https://mlsanity.com/paper/2604.07869/) (2026)

## Abstract (arXiv)

To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in power usage while keeping the reliability and robustness of the test results high. We tested our method on 5 recommender system algorithms across 6 datasets and compared it with 10-fold cross validation. On average e-fold cross validation only needed 41.5% of the energy that 10-fold cross validation would need, while it's results only differed by 1.81%. We conclude that e-fold cross validation is a promising approach that has the potential to be an energy efficient but still reliable alternative to k-fold cross validation.