  System-provided explanations for recommendations are an important component
towards transparent and trustworthy AI. In state-of-the-art research, this is a
one-way signal, though, to improve user acceptance. In this paper, we turn the
role of explanations around and investigate how they can contribute to
enhancing the quality of the generated recommendations themselves. We devise a
human-in-the-loop framework, called ELIXIR, where user feedback on explanations
is leveraged for pairwise learning of user preferences. ELIXIR leverages
feedback on pairs of recommendations and explanations to learn user-specific
latent preference vectors, overcoming sparseness by label propagation with
item-similarity-based neighborhoods. Our framework is instantiated using
generalized graph recommendation via Random Walk with Restart. Insightful
experiments with a real user study show significant improvements in movie and
book recommendations over item-level feedback.
