User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
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DOI：
10.48550/arxiv.2308.00894
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Juntao Tan;Yingqiang Ge;Yangchun Zhu;Yinglong Xia;Jiebo Luo;Jianchao Ji;Yongfeng Zhang
中科院分区：
文献类型：
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作者：
Juntao Tan;Yingqiang Ge;Yangchun Zhu;Yinglong Xia;Jiebo Luo;Jianchao Ji;Yongfeng Zhang
现代推荐系统利用用户的历史行为来生成个性化的推荐。然而，这些系统往往缺乏用户的可控性，导致用户对系统的满意度和信任度下降。鉴于最近可解释推荐系统的进步，增强了用户对推荐机制的理解，我们建议利用这些进步来提高用户的可控性。在本文中，我们提出了一个用户可控的推荐系统，在一个统一的框架内无缝地集成了可解释性和可控性。通过反事实推理提供回顾性和前瞻性解释，用户可以通过与这些解释交互来定制他们对系统的控制。此外，我们引入并评估了推荐系统中可控性的两个属性：可控性的复杂性和可控性的准确性。对MovieLens和Yelp数据集的实验评估证实了我们提出的框架的有效性。此外，我们的实验表明，向用户提供控制选项可能会在未来提高推荐的准确性。源代码和数据可从\url{https://github.com/chrisjtan/ucr}获得。
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}.