Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling

This thesis investigates dataset downsampling as a strategy to optimize energy efficiency in recommender systems while maintaining competitive performance. With increasing dataset sizes posing computational and environmental challenges, this study explores the trade-offs between energy efficiency and recommendation quality in Green Recommender Systems, which aim to reduce environmental impact. By applying two downsampling approaches to seven datasets, 12 algorithms, and two levels of core pruning, the research demonstrates significant reductions in runtime and carbon emissions. For example, a 30% downsampling portion can reduce runtime by 52% compared to the full dataset, leading to a carbon emission reduction of up to 51.02 KgCO2e during the training of a single algorithm on a single dataset. The analysis reveals that algorithm performance under different downsampling portions depends on factors like dataset characteristics, algorithm complexity, and the specific downsampling configuration (scenario dependent). Some algorithms, which showed lower nDCG@10 scores compared to higher-performing ones, exhibited lower sensitivity to the amount of training data, offering greater potential for efficiency in lower downsampling portions. On average, these algorithms retained 81% of full-size performance using only 50% of the training set. In certain downsampling configurations, where more users were progressively included while keeping the test set size fixed, they even showed higher nDCG@10 scores than when using the full dataset. These findings highlight the feasibility of balancing sustainability and effectiveness, providing insights for designing energy-efficient recommender systems and promoting sustainable AI practices.

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