  Recommender systems are nowadays a pervasive part of our online user
experience, where they either serve as information filters or provide us with
suggestions for additionally relevant content. These systems thereby influence
which information is easily accessible to us and thus affect our
decision-making processes though the automated selection and ranking of the
presented content. Automated recommendations can therefore be seen as digital
nudges, because they determine different aspects of the choice architecture for
users.
  In this work, we examine the relationship between digital nudging and
recommender systems, topics that so far were mostly investigated in isolation.
Through a systematic literature search, we first identified 87 nudging
mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then
shows that only a small part of these nudging mechanisms was previously
investigated in the context of recommender systems. This indicates that there
is a huge potential to develop future recommender systems that leverage the
power of digital nudging in order to influence the decision-making of users. In
this work, we therefore outline potential ways of integrating nudging
mechanisms into recommender systems.

Human-Computer Interaction;Information Retrieval