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Most of the research in the recommender systems domain is focused on the optimization of metrics based on historical data such as mean average precision or recall. However, there is a gap between the research and industry since the leading key performance indicators for businesses are revenue and profit. In this article, 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. [ABSTRACT FROM AUTHOR]

In the domain of e-commerce, acquiring a new customer is generally more expensive than keeping the existing ones. A successful prediction of churn of a specific customer provides an opportunity to change his/her decision to leave. In this paper, we propose a novel complex user model focused on the user churn intent prediction. The idea of our model is based on composing of multiple sets of features representing user's interaction with the web application. The performance of our model is evaluated indirectly by the prediction of churn, using real data from online retailers. The results show that the prediction using the proposed model outperforms the churn prediction based on baseline models across two domains. [ABSTRACT FROM AUTHOR]