There are very few works about explaining content-based recommendations of images in the artistic domain. Current works do not provide a perspective of the many variables involved in the user perception of several aspects of the system such as domain knowledge, relevance, explainability, and trust. In this paper, we aim to fill this gap by studying three interfaces, with different levels of explainability, for artistic image recommendation. Our experiments with N=121 users confirm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability and relevance. Furthermore, our results show that the observed effects are also dependent on the underlying recommendation algorithm used. We tested two algorithms: Deep Neural Networks (DNN), which has high accuracy, and Attractiveness Visual Features (AVF) with high transparency but lower accuracy. Our results indicate that algorithms should not be studied in isolation, but rather in conjunction with interfaces, since both play a significant role in the perception of explainability and trust for image recommendation. Finally, using the framework by Knijnenburg et al., we provide a comprehensive model which synthesizes the effects between different variables involved in the user experience with explainable visual recommender systems of artistic images.