Trust is long recognized to be an important factor in Recommender Systems (RS). However, there are different perspectives on trust and different ways to evaluate it. Moreover, a link between trust and transparency is often assumed but not always further investigated. In this paper we first go through different understandings and measurements of trust in the AI and RS community, such as demonstrated and perceived trust. We then review the relationsships between trust and transparency, as well as mental models, and investigate different strategies to achieve transparency in RS such as explanation, exploration and exploranation (i.e., a combination of exploration and explanation). We identify a need for further studies to explore these concepts as well as the relationships between them.

翻译：信任长期以来被认为是推荐系统（RS）中的一个重要因素。然而，关于信任存在不同的视角和评估方式。此外，信任与透明度之间的关联虽常被假定，但并未总是得到进一步探究。本文首先梳理了人工智能与推荐系统领域中对信任的不同理解与度量方式，例如已证实的信任与感知的信任。随后，我们综述了信任与透明度以及心智模型之间的关系，并研究了在推荐系统中实现透明度的不同策略，如解释、探索以及探索性解释（即探索与解释的结合）。我们指出，未来需要进一步研究以探索这些概念及其相互关系。