e-Fold Cross-Validation for Recommender-System Evaluation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916502394896384 |
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| author | Baumgart, Moritz Wegmeth, Lukas Vente, Tobias Beel, Joeran |
| author_facet | Baumgart, Moritz Wegmeth, Lukas Vente, Tobias Beel, Joeran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | e-Fold Cross-Validation for Recommender-System Evaluation Baumgart, Moritz Wegmeth, Lukas Vente, Tobias Beel, Joeran Machine Learning Information Retrieval 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. |
| title | e-Fold Cross-Validation for Recommender-System Evaluation |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2412.01011 |