  Explanations are used in recommender systems for various reasons. Users have
to be supported in making (high-quality) decisions more quickly. Developers of
recommender systems want to convince users to purchase specific items. Users
should better understand how the recommender system works and why a specific
item has been recommended. Users should also develop a more in-depth
understanding of the item domain. Consequently, explanations are designed in
order to achieve specific \emph{goals} such as increasing the transparency of a
recommendation or increasing a user's trust in the recommender system. In this
paper, we provide an overview of existing research related to explanations in
recommender systems, and specifically discuss aspects relevant to group
recommendation scenarios. In this context, we present different ways of
explaining and visualizing recommendations determined on the basis of
preference aggregation strategies.
