In recent years, researchers in the field of recommender systems have explored a range of advanced interfaces to improve user interactions with recommender systems. Some of the major research ideas explored in this new area include the explainability and controllability of recommendations. Controllability enables end users to participate in the recommendation process by providing various kinds of input. Explainability focuses on making the recommendation process and the reasons behind specific recommendation more clear to the users. While each of these approaches contributes to making traditional “black-box” recommendation more attractive and acceptable to end users, little is known about how these approaches work together. In this paper, we investigate the effects of adding user control and visual explanations in a specific context of an interactive hybrid social recommender system. We present Relevance Tuner+, a hybrid recommender system that allows the users to control the fusion of multiple recommender sources while also offering explanations of both the fusion process and each of the source recommendations. We also report the results of a controlled study (N = 50) that explores the impact of controllability and explainability in this context.

中文翻译：

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可控性和可解释性在社交推荐系统中的影响

近年来，推荐系统领域的研究人员探索了一系列高级接口，以改善用户与推荐系统的交互。在这个新领域探索的一些主要研究思想包括推荐的可解释性和可控性。可控性使最终用户能够通过提供各种输入来参与推荐过程。可解释性侧重于使推荐过程和特定推荐背后的原因对用户更加清晰。虽然这些方法中的每一种都有助于使传统的“黑盒”推荐对最终用户更具吸引力和可接受性，但人们对这些方法如何协同工作知之甚少。在本文中，我们研究了在交互式混合社交推荐系统的特定上下文中添加用户控制和视觉解释的效果。我们提出了 Relevance Tuner+，这是一个混合推荐系统，允许用户控制多个推荐源的融合，同时还提供融合过程和每个源推荐的解释。我们还报告了一项对照研究（N = 50）的结果，该研究探讨了在这种情况下可控性和可解释性的影响。