# Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance

arXiv: https://arxiv.org/abs/2410.09359 · HTML: https://mlsanity.com/paper/2410.09359/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Efficient Machine Learning for Big Data: A Review](https://mlsanity.com/paper/1503.05296/) (2015)
- [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)
- [Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization](https://mlsanity.com/paper/2008.13532/) (2020)
- [Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks](https://mlsanity.com/paper/2404.01965/) (2024)
- [EMERS: Energy Meter for Recommender Systems](https://mlsanity.com/paper/2409.15060/) (2024)
- [Evaluating the performance-deviation of itemKNN in RecBole and LensKit](https://mlsanity.com/paper/2407.13531/) (2024)

## Built on by (most cited first, 4 of 4)

- [e-Fold Cross-Validation for Recommender-System Evaluation](https://mlsanity.com/paper/2412.01011/) (2024)
- [Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics](https://mlsanity.com/paper/2508.04419/) (2025)
- [Extending Dataset Pruning to Object Detection: A Variance-based Approach](https://mlsanity.com/paper/2505.17245/) (2025)
- [Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems](https://mlsanity.com/paper/2604.07869/) (2026)

## Abstract (arXiv)

As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques in the context of Green Recommender Systems. We conducted experiments on the MovieLens 100K, 1M, 10M, and Amazon Toys and Games datasets, analyzing the performance of various recommender algorithms under different portions of dataset size. Our results indicate that while more training data generally leads to higher algorithm performance, certain algorithms, such as FunkSVD and BiasedMF, particularly with unbalanced and sparse datasets like Amazon Toys and Games, maintain high-quality recommendations with up to a 50% reduction in training data, achieving nDCG@10 scores within approximately 13% of full dataset performance. These findings suggest that strategic dataset reduction can decrease computational and environmental costs without substantially compromising recommendation quality. This study advances sustainable and green recommender systems by providing insights for reducing energy consumption while maintaining effectiveness.