  Among the existing approaches for visual-based Recommender System (RS)
explainability, utilizing user-uploaded item images as efficient, trustable
explanations is a promising option. However, current models following this
paradigm assume that, for any user, all images uploaded by other users can be
considered negative training examples (i.e. bad explanatory images), an
inadvertedly naive labelling assumption that contradicts the rationale of the
approach. This work proposes a new explainer training pipeline by leveraging
Positive-Unlabelled (PU) Learning techniques to train image-based explainer
with refined subsets of reliable negative examples for each user selected
through a novel user-personalized, two-step, similarity-based PU Learning
algorithm. Computational experiments show this PU-based approach outperforms
the state-of-the-art non-PU method in six popular real-world datasets, proving
that an improvement of visual-based RS explainability can be achieved by
maximizing training data quality rather than increasing model complexity.
