  In Recommender systems, data representation techniques play a great role as
they have the power to entangle, hide and reveal explanatory factors embedded
within datasets. Hence, they influence the quality of recommendations.
Specifically, in Visual Art (VA) recommendations the complexity of the concepts
embodied within paintings, makes the task of capturing semantics by machines
far from trivial. In VA recommendation, prominent works commonly use manually
curated metadata to drive recommendations. Recent works in this domain aim at
leveraging visual features extracted using Deep Neural Networks (DNN). However,
such data representation approaches are resource demanding and do not have a
direct interpretation, hindering user acceptance. To address these limitations,
we introduce an approach for Personalised Recommendation of Visual arts based
on learning latent semantic representation of paintings. Specifically, we
trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of
paintings. Our LDA model manages to successfully uncover non-obvious semantic
relationships between paintings whilst being able to offer explainable
recommendations. Experimental evaluations demonstrate that our method tends to
perform better than exploiting visual features extracted using pre-trained Deep
Neural Networks.
