Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.

翻译：现代推荐系统利用用户的历史行为生成个性化推荐。然而，这些系统往往缺乏用户可控性，导致用户满意度和对系统的信任度降低。鉴于可解释推荐系统的最新进展能增强用户对推荐机制的理解，我们提出利用这些进展来提升用户可控性。本文提出了一种用户可控推荐系统，将可解释性与可控性无缝集成于统一框架中。通过反事实推理提供回溯与前瞻双重解释，用户可通过与这些解释的交互自主定制对系统的控制。此外，我们引入并评估了推荐系统中可控性的两个属性：可控性复杂度与可控性精度。在MovieLens和Yelp数据集上的实验验证了所提框架的有效性。进一步实验表明，为用户提供控制选项有望在未来提升推荐精度。源代码与数据见\url{https://github.com/chrisjtan/ucr}。