论文标题
推荐系统的数字化裸体：调查和未来方向
Digital Nudging with Recommender Systems: Survey and Future Directions
论文作者
论文摘要
如今，推荐系统是我们在线用户体验的普遍部分，它们可以用作信息过滤器，要么为我们提供有关其他相关内容的建议。这些系统因此会影响我们很容易访问哪些信息，从而通过自动选择和排名来影响我们的决策过程。因此，自动化的建议可以看作是数字推荐，因为它们决定了用户选择体系结构的不同方面。 在这项工作中，我们研究了数字化裸体和推荐系统之间的关系，到目前为止，这些主题大多是孤立研究的。通过系统的文献搜索，我们首先确定了87种淡淡的机制，我们将其归类为新颖的分类法。然后，随后的分析表明，以前在推荐系统的背景下研究了这些轻度的机制的一小部分。这表明，开发未来的推荐系统具有巨大的潜力，这些系统利用数字化的力量来影响用户的决策。因此，在这项工作中，我们概述了将纽约机制集成到推荐系统中的潜在方法。
Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content. Automated recommendations can therefore be seen as digital nudges, because they determine different aspects of the choice architecture for users. In this work, we examine the relationship between digital nudging and recommender systems, topics that so far were mostly investigated in isolation. Through a systematic literature search, we first identified 87 nudging mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then shows that only a small part of these nudging mechanisms was previously investigated in the context of recommender systems. This indicates that there is a huge potential to develop future recommender systems that leverage the power of digital nudging in order to influence the decision-making of users. In this work, we therefore outline potential ways of integrating nudging mechanisms into recommender systems.
