Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance
This addresses the energy consumption problem for large-scale recommender systems, but it is incremental as it builds on existing downsampling techniques.
This paper tackles the problem of reducing the environmental impact of recommender systems by optimizing dataset sizes for energy efficiency, finding that certain algorithms can maintain high-quality recommendations with up to a 50% reduction in training data, achieving nDCG@10 scores within about 13% of full dataset performance.
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.