Logic-Logit：一种基于逻辑的选择建模方法
Logic-Logit: A Logic-Based Approach to Choice Modeling
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中文总结 AI 辅助
本研究提出基于逻辑规则的可解释选择模型Logic-Logit，结合列生成与Frank-Wolfe算法提取规则，在合成及真实数据上显著提升可解释性与准确性。
中文摘要 AI 辅助
在本研究中，我们提出了一种新颖的基于规则的、可解释的选择模型Logic-Logit，旨在有效地学习和解释人类的选择行为。选择模型已被广泛应用于各个领域，例如商业需求预测、推荐系统和消费者行为分析，通常被归类为参数化、非参数化或基于深度网络的模型。尽管近期的创新倾向于采用神经网络方法以利用其计算能力，但这些灵活的模型往往涉及大量参数且缺乏可解释性，从而限制了它们在需要透明度的场景中的有效性。先前的经验证据表明，个体通常使用启发式决策规则来形成他们的考虑集，然后从中进行选择。这些规则通常表示为合取项的析取（即AND的OR）。这些由规则驱动的“先考虑后选择”的决策过程使人们能够快速筛选大量备选方案，同时降低认知和搜索成本。受此洞察的启发，我们的方法利用逻辑规则来阐明人类的选择，为偏好建模提供了新的视角。我们引入了列生成技术与Frank-Wolfe算法的独特组合，以促进偏好建模中规则的高效提取——这一过程被认为是NP难的。我们在合成数据集以及来自商业和医疗保健领域的真实世界数据上进行的实证评估表明，Logic-Logit在可解释性和准确性方面显著优于基线模型。
英文摘要
In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice models have been widely applied across various domains---such as commercial demand forecasting, recommendation systems, and consumer behavior analysis---typically categorized as parametric, nonparametric, or deep network-based. While recent innovations have favored neural network approaches for their computational power, these flexible models often involve large parameter sets and lack interpretability, limiting their effectiveness in contexts where transparency is essential. Previous empirical evidence shows that individuals usually use heuristic decision rules to form their consideration sets, from which they then choose. These rules are often represented as disjunctions of conjunctions (i.e., OR-of-ANDs). These rules-driven, consider-then-choose decision processes enable people to quickly screen numerous alternatives while reducing cognitive and search costs. Motivated by this insight, our approach leverages logic rules to elucidate human choices, providing a fresh perspective on preference modeling. We introduce a unique combination of column generation techniques and the Frank-Wolfe algorithm to facilitate efficient rule extraction for preference modeling---a process recognized as NP-hard. Our empirical evaluation, conducted on both synthetic datasets and real-world data from commercial and healthcare domains, demonstrates that Logic-Logit significantly outperforms baseline models in terms of interpretability and accuracy.
发表机构
- CUHK-Shenzhen（香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
