  Academic research in the field of recommender systems mainly focuses on the
problem of maximizing the users' utility by trying to identify the most
relevant items for each user. However, such items are not necessarily the ones
that maximize the utility of the service provider (e.g., an online retailer) in
terms of the business value, such as profit. One approach to increasing the
providers' utility is to incorporate purchase-oriented information, e.g., the
price, sales probabilities, and the resulting profit, into the recommendation
algorithms. In this paper we specifically focus on price- and profit-aware
recommender systems. We provide a brief overview of the relevant literature and
use numerical simulations to illustrate the potential business benefit of such
approaches.
