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.

翻译：当今许多在线服务会为其用户提供个性化推荐。这类推荐通常旨在满足特定用户需求，例如在信息过载情境下快速找到相关内容。相应地，该领域的学术文献主要关注推荐系统对终端用户的价值。在此背景下，一个基本假设是：通过推荐实现的改进服务将进而对组织目标产生积极影响，例如提升客户留存率或忠诚度。然而在现实中，推荐系统可通过将价格感知、盈利性等货币化考量纳入底层推荐模型，更直接地服务于组织的经济目标。本研究基于系统性综述方法，对被称为"经济推荐系统"的现有文献进行调研，共筛选出133篇相关论文。我们首先沿不同维度对现有工作进行分类，继而梳理文献中最重要的技术方法。此外，我们还探讨了评估此类系统的常见方法论，最后指出了当前研究的局限性与未来方向。