On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
This work addresses hyper-parameter selection for recommendation systems, but it is incremental as it builds on existing tuning methodologies without introducing new paradigms.
The paper investigates hyper-parameter tuning in cross-validation, focusing on its impact on accuracy and novelty across datasets like MovieLens and Amazon Movies, using baselines such as User-kNN, Item-kNN, and BPR-MF with grid search over 15 values per parameter.
Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As an example, it may affect not just accuracy but also novelty as well as it may depend on the adopted dataset. Moreover, sometimes it could be sufficient to concentrate on a single parameter only (or a few of them) instead of their overall set. In this paper we report on our investigation on hyper-parameters tuning by performing an extensive 10-Folds Cross-Validation on MovieLens and Amazon Movies for three well-known baselines: User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy considering approximately 15 values for each parameter, and we then evaluated each combination of parameters in terms of accuracy and novelty. We investigated the discriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, finally, we analyzed the role of parameters on model evaluation for Cross-Validation.