As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training these large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques. 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 performance in algorithms, certain algorithms, such as FunkSVD and BiasedMF, particularly in cases involving more unbalanced and sparse dataset like Amazon Toys and Games, maintain high-quality recommendations with up to 50% reduction in training data, achieving nDCG@10 scores within \(\sim \) 13% of their 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 actionable insights for reducing energy consumption while maintaining effectiveness.