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Hyper-parameters tuning is a crucial task to make a model perform at itsbest. However, despite the well-established methodologies, some aspects of thetuning remain unexplored. As an example, it may affect not just accuracy butalso novelty as well as it may depend on the adopted dataset. Moreover,sometimes it could be sufficient to concentrate on a single parameter only (ora few of them) instead of their overall set. In this paper we report on ourinvestigation on hyper-parameters tuning by performing an extensive 10-FoldsCross-Validation on MovieLens and Amazon Movies for three well-known baselines:User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy consideringapproximately 15 values for each parameter, and we then evaluated eachcombination of parameters in terms of accuracy and novelty. We investigated thediscriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, finally,we analyzed the role of parameters on model evaluation for Cross-Validation.

On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them