On the estimation of discrete choice models to capture irrational customer behaviors
捕捉非理性顾客行为的离散选择模型估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究消费者离散选择、随机效用与广义随机偏好模型的估计，最直接对应离散选择领域。
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捕捉非理性顾客行为的离散选择模型估计
- 研究问题
- 当顾客选择存在随机效用最大化框架难以描述的光环效应时，如何估计更灵活的选择模型，并控制灵活性带来的过拟合风险？
- 方法与思路
- 作者使用广义随机偏好模型和部分排序偏好表示顾客类型，借助列生成与树状结构提取类型，并以支配规则优先保留低阶产品交互。
- 材料陈述的贡献
- 摘要称，该方法能同时描述理性与非理性类型，并在大型零售连锁数据的实验中报告纳入非理性偏好后的平均预测准确度提升。
- 阅读关注点
- 应核对顾客类型与部分排序的定义、列生成停止准则、支配规则的限制，以及预测比较的数据划分、基准和过拟合控制。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明准确度指标和验证设计，单一零售数据的提升不能直接推广。
原文摘要
The Random Utility Maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economics has provided strong empirical evidence of irrational choice behavior, such as halo effects, that are incompatible with this framework. Models belonging to the Random Utility Maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed Generalized Stochastic Preference choice model, which subsumes the family of Random Utility Maximization models and is capable of capturing halo effects. Specifically, we show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a tree-like data structure containing the customer behaviors. Further, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average, when tested on a real-world dataset from a large chain of grocery and drug stores.