超越相关性:利用反事实数据增强对用户旅行决策的因素级因果解释

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hanzhe Li, Jingjing Gu, Xinjiang Lu, Dazhong Shen, Yuting Liu, YaNan Deng, Guoliang Shi, Hui Xiong
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引用次数: 0

摘要

兴趣点(POI)推荐是城市计算领域的一个重要研究热点,在城市建设中发挥着至关重要的作用。由于城市出行场景中的影响因素复杂多样,理解用户的出行决策过程并探索兴趣点选择的因果关系并非易事。此外,严重的数据稀缺性所导致的虚假解释,即把普遍相关性误解为因果关系,也可能阻碍我们理解用户的出行决策。为此,我们在本文中提出了一种基于反事实数据增强的用户出行决策因素级因果解释生成框架,命名为用户出行决策因素级因果解释(FCE-UTD),它可以区分真假因果因素并生成真实的因果解释。具体来说,我们首先假设用户决策是由一系列不同因素组成的。然后,通过联合反事实对比学习范式保留用户决策结构,我们学习因素的表征并检测相关因素。接下来,我们通过反事实表征生成器构建反事实决策,进一步识别真正的因果因素,特别是,它不仅可以增强数据集,缓解稀疏性,还有助于从其他可能导致虚假解释的虚假因果因素中澄清因果因素。此外,还提出了一种因果依赖学习器,通过学习因果依赖分数来识别每个决策的因果因素。在三个真实世界数据集上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到率、保真度和下游任务方面的优越性。额外的案例研究也证明了 FCE-UTD 在 POI 选择中生成因果解释的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation

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 paper, 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.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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