Algorithmic and HCI Aspects for Explaining Recommendations of Artistic Images

Explaining suggestions made by recommendation systems is key to make users trust and accept these systems. This is specially critical in areas such as art image recommendation. Traditionally, artworks are sold in galleries where people can see them physically, and artists have the chance to persuade the people into buying them. On the other side, online art stores only offer the user the action of navigating through the catalog, but nobody plays the persuading role of the artist. Moreover, few works in recommendation systems 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 article, we aim to fill this gap by studying several aspects of the user experience with a recommender system of artistic images, from algorithmic and HCI perspectives. We conducted two user studies in Amazon Mechanical Turk to evaluate different levels of explainability, combined with different algorithms. While in study 1 we focus only on a desktop interface, in study 2 we attempt to understand the effect of explanations in mobile devices. In general, our experiments confirm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability and relevance. In the first study, our results show that the observed effects are dependent on the underlying recommendation algorithm used. In the second study, our results show that these effects are also dependent of the device used in the study but with a smaller effect. Finally, using the framework by Knijnenburg et al., we provide a comprehensive model, for each study, which synthesizes the effects between different variables involved in the user experience with explainable visual recommender systems of artistic images.