Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.

兴趣点（POI）推荐是城市计算领域的重要研究热点，在城市建设中发挥着至关重要的作用。而城市出行场景影响因素复杂多样，了解用户出行决策过程并探究POI选择的因果关系并不容易。此外，严重的数据稀疏性导致的虚假解释，即将普遍相关性错误地表述为因果关系，也可能阻碍我们理解用户的旅行决策。为此，在本文中，我们提出了一种基于用户旅行决策的反事实数据增强的因子级因果解释生成框架，称为用户旅行决策的因子级因果解释（FCE-UTD），该框架可以区分真实和真实的因果解释。错误的因果因素并产生真实的因果解释。具体来说，我们首先假设用户决策由一组几个不同的因素组成。然后，通过使用联合反事实对比学习范式保留用户决策结构，我们学习因素的表示并检测相关因素。接下来，我们通过使用反事实表示生成器构建反事实决策来进一步识别真正的因果因素，特别是，它不仅可以扩充数据集并减轻稀疏性，而且还有助于从其他可能导致虚假解释的错误因果因素中澄清因果因素。此外，还提出了因果依赖学习器，通过学习因果依赖分数来识别每个决策的因果因素。对三个现实世界数据集进行的广泛实验证明了我们的方法在不同行为场景下的签入率、保真度和下游任务方面的优越性。额外的案例研究还证明了 FCE-UTD 在 POI 选择中生成因果解释的能力。