Summary of E-fold Cross-validation For Recommender-system Evaluation, by Moritz Baumgart et al.
e-Fold Cross-Validation for Recommender-System Evaluation
by Moritz Baumgart, Lukas Wegmeth, Tobias Vente, Joeran Beel
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to k-fold cross validation, dubbed e-fold cross validation, is proposed as a more energy-efficient alternative for recommender systems. This method seeks to minimize the number of folds required while maintaining the reliability and robustness of test results. To evaluate its effectiveness, the authors tested e-fold cross validation on 5 recommender system algorithms across 6 datasets and compared it with traditional 10-fold cross validation. The results showed that e-fold cross validation requires significantly less energy (41.5% reduction) while only differing by 1.81% in terms of test accuracy. This suggests that e-fold cross validation has the potential to be a reliable and energy-efficient alternative for recommender systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to check how well recommender systems work is being developed. It’s called e-fold cross validation, and it aims to use less energy than the usual method while still getting good results. The developers tested this new approach on several algorithms and datasets and found that it uses 41.5% less energy than the traditional method. The results were only slightly different, which means this new way might be a good alternative. |
