Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users' decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user' s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.

翻译：近年来，推荐系统不仅致力于提供准确的推荐，还旨在生成有助于用户更好理解推荐结果的解释。然而，现有的大多数可解释推荐方法仅考虑评论中内容（如词语或方面）的重要性，而忽略了它们之间的顺序关系。这种忽视导致遗漏了人类决策过程中的关键顺序维度，从而造成性能欠佳。因此，本文受认知与决策心理学中的顺序效应理论启发，提出了一种基于方面顺序树（Aspect Order Tree，AOTree）的可解释推荐方法，以捕捉决策因素之间的依赖关系。我们首先通过分析用户评论，验证了该理论在推荐场景中的适用性。随后，依据该理论，所提出的AOTree方法扩展了决策树的构建过程，以捕捉用户决策过程中的方面顺序，并利用注意力机制基于方面顺序进行预测。大量实验表明，我们的方法在评分预测任务上具有显著效果，并且通过按特定顺序展示解释，能够更一致地贴合用户的决策过程，从而增强了模型的可解释性。