  Many of today's online services provide personalized recommendations to their
users. Such recommendations are typically designed to serve certain user needs,
e.g., to quickly find relevant content in situations of information overload.
Correspondingly, the academic literature in the field largely focuses on the
value of recommender systems for the end user. In this context, one underlying
assumption is that the improved service that is achieved through the
recommendations will in turn positively impact the organization's goals, e.g.,
in the form of higher customer retention or loyalty. However, in reality,
recommender systems can be used to target organizational economic goals more
directly by incorporating monetary considerations such as price awareness and
profitability aspects into the underlying recommendation models. In this work,
we survey the existing literature on what we call Economic Recommender Systems
based on a systematic review approach that helped us identify 133 relevant
papers. We first categorize existing works along different dimensions and then
review the most important technical approaches from the literature.
Furthermore, we discuss common methodologies to evaluate such systems and
finally outline the limitations of today's research and future directions.
