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.

翻译：为应对推荐系统日益增长的能耗问题，我们实现了一种新颖的k折交叉验证替代方案。该方案命名为e折交叉验证，旨在通过最小化折数来降低能耗，同时保持测试结果的可靠性与鲁棒性。我们在6个数据集上对5种推荐系统算法测试了该方法，并与10折交叉验证进行对比。平均而言，e折交叉验证仅需10折交叉验证41.5%的能耗，而结果差异仅为1.81%。我们得出结论：e折交叉验证是一种具有潜力的方法，有望成为k折交叉验证在能效与可靠性方面兼顾的替代方案。