  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.
