  Most of the research in the recommender systems domain is focused on the
optimization of the metrics based on historical data such as Mean Average
Precision (MAP) or Recall. However, there is a gap between the research and
industry since the leading Key Performance Indicators (KPIs) for businesses are
revenue and profit. In this paper, we explore the impact of manipulating the
profit awareness of a recommender system. An average e-commerce business does
not usually use a complicated recommender algorithm. We propose an adjustment
of a predicted ranking for score-based recommender systems and explore the
effect of the profit and customers' price preferences on two industry datasets
from the fashion domain. In the experiments, we show the ability to improve
both the precision and the generated recommendations' profit. Such an outcome
represents a win-win situation when e-commerce increases the profit and
customers get more valuable recommendations.
